research topic about business in pandemic quantitative

• Open access
• Published: 30 September 2022

Management research and the impact of COVID-19 on performance: a bibliometric review and suggestions for future research

• Kingsley Opoku Appiah 1 ,
• Bismark Addai 2 ,
• Wesley Ekuban 3 ,
• Suzzie Owiredua Aidoo 4 &
• Joseph Amankwah-Amoah 5  

Future Business Journal volume  8 , Article number:  41 ( 2022 ) Cite this article

6781 Accesses

2 Citations

Metrics details

Although there has been a burgeoning scholarly interest in the effects of COVID-19, the current stream of research remains scattered in different business and management fields and domains. Accordingly, integrative knowledge is needed to drive poignant and relevant examinations of the phenomenon. This study attempts to fill this gap by providing a synthesis of the literature, patterns of research studies, and direction for further development of the field. This study also provides a systematic identification and bibliometric and thematic review of literature, performance analysis, science mapping, and cluster analysis. The study additionally provides suggestions for future research to guide relevant discourse.

Introduction

The term pandemic has been used to describe the widespread outbreak of disease through human-to-human infections [ 1 ]. Medical texts providing a clear definition of what constitutes a pandemic are non-existent. However, its geographic extent and infectiousness and severe negative impact on all aspects of society are clearly understood [ 2 ]. To this end, research has continued to understand the extent to which pandemics shape communities, economies and society as a whole [ 3 , 4 ]. The COVID-19 pandemic is no exception, with its catastrophic effects considered to be one of the worst in human history [ 5 ]. It is no surprise the plethora of studies seeking to understand the phenomena. The severity of the pandemic has paved the way for a rapidly escalating body of empirical literature analyzing the consequences of the COVID-19 pandemic on countries [ 6 , 7 ], firms [ 8 ], and households [ 9 , 10 ]. Some business and finance-related studies, for example, have focused on macro-economic indicators [ 11 ], policy alternatives and implementation [ 12 , 13 ], business responses and implications [ 4 , 14 ] as well as firm performance outcomes and failures [ 15 , 16 ].

The COVID-19 pandemic like all other global crises impacts on all aspects of life including business activities. Shocks caused by such events disrupt business operations across the globe and in extreme situations lead to business failure [ 17 , 18 ]. The influence of business activity on national and global economies has encouraged an increasing scholarly interest in understanding the extent to which firm performance has been impacted. Developing suitable and sustainable policy and strategy responses is the logical action and focus of all governments and scholars to help mitigate the negative effects of this pandemic. However, to develop such effective strategies, the extent and various ways in which firm activities and performance has been affected must first be examined. To this end, scholars have found that the COVID-19 pandemic has severely impacted the performance of the hospitality industry [ 19 ], supply chains [ 20 ], stocks of listed companies [ 21 , 22 ], SMEs and family firms [ 23 , 24 ].

Notwithstanding the vital contributions of these studies, the integral role of research is to detect and synthesize patterns, conditions and effects in business activity, to help ensure effective decision-making and policy development. Carracedo et al. [ 25 ] began this pattern detection by conducting a systematic literature review of relevant literature. Although, Carracedo et al.’s [ 25 ] study offers novel insights into the clusters of COVID-19 business-related studies, it hardly provides in-depth knowledge and practicable knowledge on the scope, relationships and gaps in literature. To this end, the present study advances knowledge by conducting a systematic literature review and bibliometric analysis of the relationship between COVID-19 and firm performance.

The contribution to the literature is threefold. First, based on the review a comprehensive baseline systematization on the impact of COVID -19 on firm performance was advanced to enhance the understanding of the impact of the pandemic on firm performance. In so doing, we also provide fruitful lines for future research and/or policy (see [ 26 ]). Second, synthesizing the rapidly evolving literature into a conceptual framework/clusters, the study provides academicians, industrial players, government agencies, and all other stakeholders a comprehensive overview and access to the central topics, trends and the implications of the research on the impact of the pandemic on firm performance. Furthermore, the review of the data provides an opportunity to offer a deeper insight to help control the impact of the pandemic on firm’s performance and the antecedent effects on households and economies.

The rest of the paper proceeds as follows. “ Method and initial statistics ” section discusses the method. “ Bibliometric analysis ” and “ Thematic/cluster analysis ” sections present an in-depth bibliometric and cluster/thematic analyses of the dataset, respectively. “ Directions for future research ” and “ Conclusion and limitations ” sections provide direction for future research and the conclusion, respectively.

Method and initial statistics

The objective of this study is to construct a scientific map and further analyze the worth of knowledge produced by management experts who examine the impact of COVID-19 on firms’ performance. Following relevant literature (e.g., [ 27 , 28 ]) and best practices in the mapping of scientific knowledge, we conduct a bibliometrix analysis and a systematic review of the relevant literature. Specifically, we use the bibliometrix to construct scientific mapping to highlight the knowledge base, and its intellectual structure as well as both the conceptual and social network structures of Covid 19’s impact on performance extant literature (see [ 29 , 30 ]). By combining the two complementary approaches, we are able to paint a picture of the development of scientific knowledge on the impact of COVID-19 on firm performance using quantitative bibliometrix tools and also provide a comprehensive analysis of the themes/topics and contents by means of qualitative systematic review. These approaches are well established in management literature (see [ 27 , 31 ]).

Data collection

To undertake this systematic and bibliometric analysis, articles discussing the influence of COVID-19 on firm performance were retrieved and analyzed. To ensure and maintain an unbiased and high-quality database and review, strict criteria were adhered to. These are described in the following steps:

Step 1 A literature search was conducted in Scopus Database. The aim is to ensure broader access to ranked management related, reputable and quality journals.

Step 2 The search was conducted with the search term: ("COVID-19" or "CoronaVirus") and ("value" or "performance" or “profitability”). Various search strings using multiple combinations of the search terms were used during the data collection process. These include ((covid-19 OR coronavirus) AND (value OR performance OR profitability)).

Step 3 The search focused on scholarly studies relating to the impact of COVID 19 on firms published from 2019 to 24 July, 2021. We consider papers published from 2019 to 2021 because COVID-19 emerged as a global health crisis in 2019 [ 9 ] and we finished our literature search in July 2021.

Step 4 These studies were limited to final articles published within Business & Management & Accounting, Social Science, Economics, Econometrics & Finance Journals. We omit books, Ph.D. Thesis, working papers, technical reports, conference proceedings. The stages and the tasks undertaken during the literature search are summarized in Table 1 .

An initial search produced 19,645 articles, excluding articles outside of the 2019–2021 range yielded 18,710. Of these 1571 were management and accounting related. Subsequently, review papers (70), conference papers (59), editorial (10), book chapters (14), note (27), book (7) and letters (4) leaving 1372 articles. Finally, 1355 English journal articles were retained for the bibliometric and thematic analyses. Subsequent steps in this study consisted of conducting a bibliometric and a thematic analysis of articles retained. The bibliometric analysis consists of a performance analysis of articles, authors and journals to identify relevant literature in the field. Next a scientific mapping of country production as well as a keyword analysis is conducted to highlight on the various research topics in the field. We use Biblioshiny and VOSViewer Software applications to perform the bibliometric analysis. Finally, the study conducts a detailed thematic analysis, by identifying and synthesizing studies within the four main research clusters.

Descriptive statistics

Table 2 captures the description of the data collected for the bibliometric analysis and the SLR. As illustrated in the table, we identify 1355 scholarly articles, spread across 437 sources, with 73,478 references, 4407 authors keywords, 3538 authors, 0.383 article per author, and 2.85 co-authors per article. Our descriptive also shows 253 single authorships, while collaboration index is 3.03 (see Table 2 ). Our analysis shows the impact of Covid 19 on performance was first mentioned in the Management research literature in 2019 by Sterling and Merluzzi’s (2019) paper, highlighting that tryouts may rise due to Covid 19-related impacts on US Firms. Table 2 reports an astronomical rate of 31.7% for the 2-year period from 2020 to 2021, implying the ever-increasing literature on the impact of Covid 19 on performance.

The study examines the scientometric index measuring a journal’s impact by assessing the average number of article citations over the last two years. Table 3 shows the top ten most impactful journals in terms of the number of publications on impact of Covid-19 on performance research. It should be noted that the top ten journals published 215 out of 1355 articles, accounting for 15.9% of articles in our dataset. The results indicate that majority of these journals ranked three or two in Association of Business School Journal quality list. Overall, these multi-disciplinary outlets suggest the topic is attractive to all management scholars and research areas.

Bibliometric analysis

The study’s bibliometric analysis reveals the valuable insights to knowledge within management research in assessing the influences of COVID on business performance.

Author influence

This section discusses the most impactful authors to the research domain. Table 4 displays the most impactful author on the basis of h-index, m-index, g-index and number of publications. We examine author’s impact by analyzing the number of academic benchmark performance indicators, namely citations, H-index, G-index, and M-index [ 32 , 33 , 34 ]. The H-index, viewed as an unbiased overview, for example, combines the number of papers and citations to assess author’s scientific contributions over time [ 35 ]. The H-index, however, overlooks the impactful but discriminatory author, implying it favors high-volume authors. Accordingly, we complement the H-index analysis with the G-index. G-index measures the highest rank such that the top G papers have, together, at least G 2 citations (see [ 35 ] for details).

Overall, the small number of publications from these prolific authors confirms the infancy stage of the research domain, thereby allowing different authors with diverse expertise in management research to contribute to the discourse on the impact of Covid-19 and performance from 2019 to present. The results indicate that Dmitry Ivanov is the most influential author with an H-index of 5 and a G-index of 6. All 6 articles authored or co-authored by him have received a total of 448 citations. This is followed by Vanessa Ratten with H and G-indexes of 5 and 6, respectively. She has authored and co-authored a total of 8 articles in the field, earning a total citation score of 64. Next, Andres Coca-Stefaniak, Sertan Kabadayi and Jungkeun Kim assume the third, fourth and fifth most impactful author, respectively, each with an H and G-index of 3. Generally, the H and G-indexes produce different rankings, the results show that the ranking remains the same except for the highest and second highest ranked authors, who interchange according to the index understudy.

Again, critics argue that both H-index and G-index ignore different career lengths [ 36 ]. Accordingly, we use the M-index (i.e., M-quotient), which is H-index divided by author’s active literary years to help provide a clearer view of author rankings in the research area. Ranking scholars according to the M-quotient, sees Junkeun Kim as the highest ranked author with an M-quotient/index of 3, Dmitry Ivanov and Vanessa Ratten are the second and third highest ranked authors. Other impactful authors, who have contributed immensely to the field and are ranked among the top 10 impactful scholars are summarized in Table 4 .

Geographical and institutional scientific production

Biblioshiny was used to retrieve the author organization/affiliation and address information, after which all the authors were sorted in descending order. Table 5 displays the top 20 organizations publishing the most articles on Covid 19 and performance. University of Johannesburg in South Africa contributes most with 17 articles on Covid 19 and performance, followed by The Hong Kong Polytechnic University in Hong Kong . Table 4 shows that RATTEN V from the La Trobe University contributes most with 9 articles, and the affiliation appears as the Top 6 contributing organization. Surprisingly, most of the other influential authors do not have their organizations listed in Table 5 . For example, Ivanov D, Gupta S, Kim J, Kumar A, Li S, Li Z, Sharma A, Zhang J, emerge as the Top 2 to 5 contributing authors, respectively; however, their organizations do not appear in influential organizations in Table 5 . Likewise, the University of Johannesburg emerges as the Top 1 contributing institution with 17 publications but no author from this university is listed in Table 4 . Similar surprises exist at other universities such as the RMIT university , the University of Auckland and the Auckland University of Technology which appear in Table 5 but no author from those organizations appears in Table 4 . The results imply that the contributing authors have come diverse research backgrounds in different methodological and industrial settings. For instance, the 17 publications from the University of Johannesburg cover various industries such as fruit, food, manufacturing and food, and they employ differing methodologies including empirical analysis and case studies.

Being a global health issue that has induced economic and social upheaval worldwide, COVID-19 and its related performance impact has received global scholarly attention. The geographic distribution of research attention is shown in Fig.  1 . The United States of America has the highest concentration of COVID-19 and performance impact studies of 377 (22.93%) articles. The literature on performance impact of COVID-19 has received much attention in the United States because the country has encountered disproportionately elevated levels of economic fallout, infections and deaths [ 6 , 37 ]. For instance, WHO reports that compared to other countries, the USA has the highest infection and death rates of 99,085,620, and 2,377,656, respectively. The United Kingdom emerges as the second country with concentration of studies on the performance impact of COVID-19 contributing 286 (17.40%) of the articles. China, Australia and India contribute the third (11.50%), fourth (10.95%) and fifth (10.10%) positions, respectively. Also, the impact of Covid-19 is evaluated in the Indonesia, Italy, New Zealand, Spain and Portugal by other 446 empirical research articles, representing 27.14% of the top 10 geographic dispersion. Generally, the geographical distribution in Fig.  1 depicts that countries in North America (USA), Oceania, Europe and Asia have the highest interest in studies on the performance impact of COVID-19 as compared to those in South America, Africa and Antarctica (where the interest in the study area is scanty). However, literature shows the highly interrelated nature of the global economy today [ 38 ]. Besides, the multinational nature of business activities today, heightens the need to expand knowledge on performance impacts across all continents. Thus, more empirical analysis on the impact of Covid-19 on performance is needed in those regions seemingly underrepresented in scholarship but pivotal to global business and supply chain [ 39 , 40 ].

Geographic dispersion of publications on Covid-19 and performance

Keyword analysis

Next, we conduct a keyword analysis to identify popular research perspectives in the area. Table 6 contains the biblioshiny’s keyword analysis results of 4407 author keywords in the 1355 articles reviewed in the study. As expected, COVID-19 and several iterations of the virus, crisis, crisis management and resilience make up the top 8 keywords. “ Tourism” takes 9th place on the list, indicating the relevance of tourism to national and global economies and elucidating the extent to which the pandemic has influenced the valuable global industry [ 41 ]. Innovation and resilience have been frequently discussed in conjunction with COVID-19 and business research, highlighting inherent relations between innovation and resilience in the COVID-19-performance nexus [ 42 , 43 ]. Again, Finsterwalder and Kuppelwieser [ 44 ] and Golan et al. [ 39 ] highlight the need for resilience and for developing appropriate strategy for recovery following a disruptive event. “Crisis Management” and “Leadership” are similarly popular research foci owing to the significant roles crisis management and leadership play in mitigating crisis and lessening its adverse effects [ 45 , 46 ]. Other frequently used keywords include performance, entrepreneurship, stock market, and supply chain as they all relate to business management and the performance implications of the pandemic. Country-related keywords include India and China, which have both been epicenters of the pandemic at one point or another [ 47 ]. Lastly, Table 6 shows gender as a frequently used keyword as a result of the recorded differences in infection and fatality rate among men and women [ 48 ] as well as employment, equality and other sociological equality implications [ 49 ].

To further identify themes investigated in the management research and the impact of COVID -19 and Performance, given that the field is at its infancy, we used co-occurrence of keywords analysis based on keywords that occurred at least five times, resulting in 133 satisfying the threshold out of 4407 authors keywords. Figure  2 displays the network visualization diagram of VOSviewer highlighting seven common keywords, namely, COVID-19, pandemic, coronavirus, resilience, crisis, COVID-19 pandemic and crisis management. The size of nodes and thickness of line displayed in Fig.  2 verify these findings (see [ 50 ], p. 552 for further reading). The homogeneous nature of these keywords confirms the collective focus on business performance and economic impacts of the pandemic. Figure  2 also displays 7 clustered keywords for 132.

Network visualization diagram of authors keyword on Covid-19 and performance

Citation analysis

In analyzing the 1355 articles, the study examined the citation measure of each article. Citation analysis is usually measured using two indexes: the global and local citations, whereas the former represents the citation of a given article by other articles within the entire academic database of articles, the latter indicates the citation score of a given article by other articles within the articles being assessed in this study [ 29 ].

Table 7 reports the top 10 cited articles, based on both local and global citation scores. The table shows that the most impactful article is “Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis of the COVID-19/SARS-CoV2 case" —[ 51 ] . Ivanov [ 51 ] examines the global supply chain impacts of pandemic outbreak through simulation-based analysis, which was published in Transportation Research Part E: Logistics and Transportation Review. The study focuses on crucial management and performance issues affected during crisis such as risk management and resilience and thus sets the tone for the later empirical studies. The second most cited article is “ Risk perceptions of COVID-19 around the world ”—[ 52 ]. This article mapped and modeled the risk perception of COVID-19 around 10 countries (US, UK, Australia, Germany, Spain, Italy, Japan, South Korea, Mexico, and Sweden). It highlights the role strong predictive roles of various experiential, and socio-cultural values and factors. The next highest cited article is “ Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research ”—[ 53 ]. The study critically evaluates tourism transformation and impacts of the pandemic through a literature review and sets the tone for resetting and advancing research frontiers.

Other impactful articles include “ Feverish stock price reactions to COVID-19 ”—[ 21 ], which provides evidence of the impacts of COVID-19 on stock returns across US industries. The next is “ Effects of COVID-19 on hotel marketing and management: a perspective article ”—[ 54 ] which discusses the effect of COVID-19 on hotel marketing and management by outlining relevant research agenda to foster knowledge development. The multidisciplinary nature of COVID-19 research is evidenced in the diverse perspective from which these studies have examined the effect of the pandemic. Table 2 summarizes the top 10 most impactful studies.

The top 10 cited articles on COVID-19 and performance are presented in Table 8 . The table indicates that the two most cited authors are Ivanov D and Wen J. who appear as the Top 2 and Top 18, respectively, in the authors with most publication list in Table 4 . Most of the other authors in the most cited author list also appear in the most productive author list and these results signify that most of the authors in the two lists are not only productive but they are also very influential.

Co-citation analysis

We analyze the 1355 articles in our dataset, with a minimum threshold of 3 citations; the obtained set contains 29 cited references out of the 6724 total references as reported by VOSviewer. The five most connected references (Fig.  3 ) are: Fornell, C. and Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error.  Journal of marketing research ,  18 (1), pp. 39–50. Henseler, J., Ringle, C.M. and Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling.  Journal of the academy of marketing science ,  43 (1), pp. 115–135. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies.  Journal of applied psychology ,  88 (5), p. 879. Sheth, J., 2020. Business of business is more than business: Managing during the Covid crisis.  Industrial Marketing Management ,  88 , pp. 261–264. Teece, D.J., Pisano, G. and Shuen, A., 1997. Dynamic capabilities and strategic management.  Strategic management journal ,  18 (7), pp. 509–533.

Network visualization of the largest connected sets of cited references

Figure  3 displays the network visualization of the largest connected sets of cited references. The papers with the highest coupling strengths are those by Henseler, J., Ringle, C.M. and Sarstedt, M., 2015 and Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P., 2003. These papers are central because of their specific contribution to methodological issues in business management and accounting research. Henseler et al. [ 55 ] provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling, while Podsakoff et al. [ 56 ] provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

Out of the 2354 cited sources, 17 journal each received more than 20 citations. The top 5 journals with the highest numbers of citations are: Tourism Management (98), International Journal of Hospitality Management (66), Journal of Business Research (50), Annual of Research (38), and Journal of Travel Research (32) (Fig.  4 ). These numbers make it evident how much of the discussion of the impact of Covid-19 on performance is supported by papers published in Tourism Management. The analysis of the network visualization provides interesting considerations. It suggests the existence of four different clusters, regarding the managerial implications related to tourism, finance, marketing and hospitality management. The specific clusters on tourism and hospitality management is inevitable due to the impact of Covid-19 on this industry.

Network visualization of the largest connected sets of cited sources by journal

Out of the 9338 cited authors, only 52 had been cited more than 10 times, while only 9 authors were cited more than 20 times. The authors are: Hall C.M (38), Boccia, F (31), Narayan, P.K (31), Salisu, A.A (28), Glossling, S (25), Zhang y (25), Liu Y (22) and Sarstedt, M (20) from University of Canterbury, Parthenope University of Naples, Deakin University, University of Ibadan, Lund University, Harbin Institute of Technology, University of Illinois, Otto-von-Guericke University, respectively. Figure  5 shows network visualization of the author co-citation analysis. It also highlights that these authors are most connected as well as most cited. The network visualization displays the existence of five different clusters. The red, green, and blue clusters are characterized by a high degree of bibliographic coupling with 15–16 items each, while two clusters show 2 items each. The red, green, blue, yellow and violet clusters with the highest degree of bibliographic coupling contain Hall et al. [ 57 ], Zhang et al. [ 58 ], and Narayan et al. [ 59 ], respectively. These studies provide insights on the impacts of Covid-19 on the services sector and consumption displacement (Hall et al. 57 ), global financial markets (Zhang et al. 58 ) and economic stimulus (Narayan et al. 59 ) (Fig.  5 ).

Network visualization of the largest connected sets of cited sources by authors

Network visualization of the bibliographic coupling of articles

Bibliographic coupling

We use bibliographic coupling of the 1349 articles to understand the theoretical foundations of publications on Covid-19 and performance. The minimum number of two articles was analyzed, resulting in the most extensive set of connected documents of 418 publications (30.98% of the dataset). Figure  6 shows network visualization of the bibliographic coupling analysis by articles, highlighting two clusters with the largest set of connected articles of 8 publications (0.59% of the dataset) implying the absence of a consolidated Covid-19 on performance field of study. The red cluster is characterized by a high degree of bibliographic coupling with 5 publications (i.e., [ 60 , 61 , 62 , 63 ]) (Tavares et al. 64 ), while green cluster shows 3 publications (i.e., Asian et al. 65 ; Kells 66 ; Tavares et al. 67 ).

Network visualization of the bibliographic coupling of journals

To analyze the bibliographic coupling of journals, we set a minimum of two articles per journal (see [ 28 ]) (Ferreira 68 ), resulting in 226 (50.78% of the dataset) out of 445 journals (see Fig. 7 ). Figure  7 shows network visualization of the bibliographic coupling analysis by journals, highlighting seven clusters with the largest set of connected journals of 74, implying the absence of a consolidated Covid-19 on performance field of study. Figure  7 reveals that the five journals with the highest bibliographic coupling index are Research in International Business, Financial Innovation, Technological Forecasting and Social Change, Tourism Management, and Journal of Business Research. Figure  7 reveals the central role played by other journal in the various clusters including red (International Journal of Contemporary Hospitality Management and Journal of Public Budgeting, Accounting and Financial Management), violet (International Journal of Entrepreneurship Behavior and research), yellow (Corporate Governance-Bingley), green (Journal of Theoretical and Applied Electronic Research) and blue (International Journal of Emerging Markets). These show the field of the research on the impact of Covid 19 on financial and non-financial indicators is receiving attention from multi-disciplinary scholars of business management and accounting.

Cluster analysis of articles

Thematic/cluster analysis

Next, to provide a deeper synthesis and identify relevant patterns in performance-related COVID-19 research, the study conducts a thematic analysis as advocated by [ 69 ]. The study begins by conducting a cluster analysis of the reviewed studies and subsequently discusses the themes of these clusters in detail. The study conducts a cluster analysis of articles to identify relevant ideas, couplings or themes shaping research in COVID-19 influence on performance research. Using a co-citation cluster analysis with Biblioshiny in R, the study identifies three main clusters of research. These are illustrated in Fig.  8 .

Although identifying the main clusters of research in this domain is important, a deeper analysis of the identified themes or clusters will offer more critical insights to knowledge while guiding direction for future research. Thus, we discuss landmark publications within each thematic cluster and synthesize their key contributions to COVID-19 and performance literature. These publications are divided into three clusters contributing to different thematic areas within COVID-19 and performance literature, as identified in Fig.  3 . The landmark publications highlighted within the first cluster assess the COVID-19 pandemic situation from various foundational perspectives. Cluster two contains articles focused on the broad concepts of crisis management and strategic management. Cluster three includes landmark publications which capture the performance outcomes and strategies of COVID-19 on various businesses including sports, education, and global supply chain.

Cluster 1: foundational discussions and risk assessment

Cluster 1 is made up of several papers that assess the COVID-19 pandemic since its inception from various perspectives. These studies elaborate on the nature and implication of the pandemic on macro-economic management [ 70 ] hospitality and human rights to movement [ 71 ] as well as service ecosystems [ 44 , 72 ]. Studies in this cluster provide an elaborate assessment of the COVID-19 pandemic, as a crisis with catastrophic implications while highlighting the shortcomings of current crisis management strategies worldwide on a business and macro-economic level.

First, we consider the seminal paper [ 71 ] which attempts to assess the impact of COVID-19 on human rights to participate in hospitality and tourism, as a result of imposed travel restrictions. The study assesses the extent to which government responses to the pandemic influenced individual right to travel for leisure, business, education among others. These were evident in the closing of tourist sites, national borders, recalling of citizens to their primary residence as well as national restrictions on movement, thus confining people to their homes with little mobility to almost all service provision locations except those considered essential. In certain instances, these restrictions resulted in the inability of some tourists to return to their home countries as was experienced on several cruise ships in Europe, the Americas and Asia. These restrictions additionally, resulted in the loss of employment of numerous individuals. However, the study shows that although the pandemic and its resultant restrictions have imposed numerous challenges to tourism, individual, business and national economic growth, the closure of national borders has seen a reduction in human trafficking, child sex tourism as well as the reduction in environmental pollution and degradation through fossil fuel consumption among others. Lastly, speculating that the global hospitality and tourism industry will face a precarious future riddled with mass closures of small hospitality businesses, increasing operating and consumer costs, the study urges that scholars continue to seek answers to important questions overtime on reinstating hospitality and tourism in a post-COVID-19 world.

Following the broad picture by Baum and Hai [ 71 ], Kabadayi et al. [ 72 ] synthesized the macro-economic impact of COVID-19 while offering a framework for recognizing impacts of disruptions on service ecosystems. The study proposes the concept of service mega-disruptions (SMDs) to refer to the simultaneous multi-industry service disruptions caused by a pandemic. Defining the concept as an event caused by an unforeseen pandemic which affects multiple stakeholders and service ecosystems simultaneously and remains difficult to swiftly recover from, the study introduces a multi-level framework which may better arm service researchers and practitioners alike for future similar disruptions. The study uncovers five research themes relevant in the reducing the impact of service mega-disruptions. These include service ecosystem recovery, service agility and transformation, service technology and automation, remote service provision and finally service theory of social distancing. The study provides these holistic themes which encompass the micro- (individual and employee), meso- (service industries and public services) and macro-level (government actions and policies) perspectives of recovery measures.

Next, Finsterwalder and Kuppelwieser [ 44 ] explore the impact of crises such as COVID-19, on the service industry and its research community. By identifying and categorizing the micro-, meso- and macro-levels of service ecosystems, the study introduces a novel resource-challenges equilibrium (RCE) framework for pre-incident, incident, and post-incident phase strategies directed at building resource resilience. The study highlights the need for stronger resilience to create, facilitate and leverage on safe co-creation spheres with consumers, businesses, not-for-profit organizations as well as governmental institutions. The study highlights the need for co-creation spheres, while accentuating the need for relevant resource-challenge balance to ensure business profitability as well all overall ecosystem equilibrium.

Next, the study discusses the article Andrew et al. [ 70 ], which explores the constraints of the Australian government in responding to crises with relevant budgetary action. The study reviews literature on the COVID-19 crisis, as well as public budgeting responses to the health and economic effects of the crisis. The study identifies public budgeting as being neoliberal. This has been evident in the duo phased response strategy to COVID-19 within the Coronavirus Economic Response Package Omnibus Bill (2020) by initially stimulating businesses through the instant asset write-off scheme in the phase first, and individuals through unemployment benefits among others in the second phase. The study in its examinations seeks to offer insights and synthesis of knowledge relevant to other countries in managing and mitigating the fiscal consequences of the COVID-19 crisis. By discussing responses, outcomes and shortcomings the study provides an overview of neoliberalism influenced crisis responses for the objective assessment of multiple governmental responses available.

Lastly, Ivanov and Dolgui [ 73 ] assess the state of global supply chain in the wake of the COVID-19 crisis and provide a methodical taxonomy of supply chain disruptions caused by pandemics. The study highlights the ideas of ripple effects, structural dynamics and network resilience relevant in the COVID-19 supply chain disruption discourse. The study assesses the various ripple effects caused by the pandemic, such as the halting of production by Chrysler Automobiles NV and Hyundai as a result of the lack of parts supplied from China. The study focuses on disruption propagation throughout networks also known as ripple effects and resultant changes within supply chain structures (structural dynamics) from an operational research perspective. The study reviews relevant theories and methodologies to disruption research at network, process and control levels. By reviewing resilience in supply chain literature, the study advocates for the consideration of 5 stages in building resilience. These include anticipation, early detection, containment, control and mitigation and finally elimination. The study thus, provides relevant direction for future research while providing foundational discourse to drive this increasingly important domain of supply chain studies.

Baudier et al . [ 74 ] use survey method to extensively examine the adoption of telemedicine solutions by patients in several countries in Europe and Asia to help avoid the spread of the disease and alleviate the associated impacts of the pandemic, while ensuring a relatively uninterrupted healthcare service provision. The study argues that the development of ICTs, the individual’s adoption rate of devices (tablets, computer, smartphones), the technological advancements of telemedicine tools, and, recently, the worldwide pandemic (COVID-19) are the key drivers of the expansion of healthcare services. The empirical results based on some constructs of the Technology Acceptance Model, Availability, Personal traits, and Perceived Risks emphasize the huge influence of Performance Expectancy, the positive impact of Contamination Avoidance and the negative effect of Perceived Risk on the adoption of Teleconsultation Solutions. Brodie et al. [ 75 ] similarly review the healthcare system, while highlighting the need for a sustained value co-creation perspective for healthcare delivery with the help of integrative technologies. This, the study argued, helps to create stronger resilience through knowledge sharing, flexibility information and learning. These studies highlight the integral need and potential ecosystem resilience has in responding to and mitigating adversity and crisis during a pandemic.

Cluster 2: crisis and strategic management

The second cluster contains a number of articles which highlights the relevance of crisis and strategic management as well as communication, coordination and the media among firms. Kraus et al. [ 24 ] in a qualitative study of family firms in five western European countries, the study examined several strategic and crisis management measures used in adapting to the crisis. The study examined strategic crisis responses including retrenchment, persevering innovating and exit as discussed by Wenzel et al. [ 4 ]. Several firms included in the study begun changing or extending (innovating) their existing business models to take advantage of new consumer demands even though they may have lost a significant portion of their typical revenue streams. Others, however, continued to persevere by maintaining existing business models as a result of extensive investments in systems prior to the pandemic. The study highlighted various changes occurring among these firms. These include an increase solidarity and commitment among employees as well as a focus on increased digitalization. More prominently, although the study provided empirical evidence for and extended the strategic responses proffered by Wenzel et al. [ 4 ], results show that firms use a combination of various coping mechanisms for two main reasons: safeguarding liquidity and improving long-term survival and viability of the company. One strategic response is incapable of achieving both objectives, thus providing a basis for the combination of various strategic responses. The study however showed that, in the beginning stages of the pandemic no firm adopted exiting as a coping mechanism.

The tourism and hospitality industry has received the greatest brunt of the pandemic and as such continues to enjoy a burgeoning interest in performance, and management research. Although various studies have focused on the performance setbacks encountered by firms within this industry [ 76 ], Sigala [ 53 ] highlights the need to effective crisis management strategies. The study attempts to provide transformational remedies by unraveling all aspects of the industry including demand, supply, and other important stakeholders through three identified stages of responding, recovering and restarting. Giousmpasoglou et al. [ 77 ] extend this conversation by highlighting the relevance of managerial roles in effective crisis management. By ensuring that managers anticipate, equip and prepare their teams for crises by identifying, monitoring and mitigating potential vulnerabilities, firms within the hospitality industry will be better placed to manage crisis and reduce economic losses. By expanding this conversation to human resource management, Carnevale and Hatak [ 78 ] advocate for greater support to the workforce as they cope with altered work systems and environments and navigate changing work-family dynamics among others.

Again, we discuss studies in this cluster that highlight how countries and institutions deal with the impacts of the pandemic through communication and media. For example, Viola et al. [ 79 ] use survey data and the logit model to examine the effectiveness of institutional communication in mitigating COVID-19 impacts in Italy. The study also highlights the crucial roles of education, health literacy and the effect of asymmetric information on the effectiveness of institutional communication. The empirical results show that education plays a significant role in understanding communication pillars and building an individual consciousness about the pandemic and its associated impacts. Similarly, Machmud [ 80 ] by means of content analysis, assesses government officials’ communication and coordination intensity on twitter social media in dealing with the impacts of Covid-19 pandemic in Indonesia. The study documents that government officials are intensively building coordination and communication to overcome the performance impact of Covid-19 in Indonesia. The study further shows that the Indonesian President constantly communicates with the national COVID-19 team to ensure that all government agencies at both central and regional levels are actively mobilized and united. The study confirms coordination and communication strategic crisis management vehicles that enable public officials to jointly implement COVID-19 control policies quickly and accurately throughout Indonesia.

We also highlight studies that contribute to the theoretical development on the use of media by individuals to deal with the impacts of the pandemic. By adopting the theory of planned behavior (TPB), Mohammed and Ferraris [ 81 ] analyze the role of social media in reducing the effects of the pandemic by specifically looking at the factors that stimulate individual’s participation in social media during crises. The empirical method from the survey data shows that attitude, perceived behavioral control, subjective norm, hedonic, utilitarian values and trust affect Twitter users’ active participation significantly during the pandemic. The understanding of these driving factors could help enhance user participation, and information dissemination in the era of social distancing and lock-downs. In the same vein, Kim [ 82 ] employs survey data to examine the effect of video games on the psychological of individuals in the era of COVID-19. The study finds that video individual’s negative and positive emotional states while playing a video game increase one’s level of psychological well-being, which also results in loyalty toward the video game. The empirical findings also indicate that individuals’ positive and negative emotional states while playing a video game were obtained from the perceived emotional value of the virtual product, implying that people evaluate the game not only based on time, money and effort, but also based on enjoyment, positive feelings and pleasure from consuming the digital product. The psychological benefits derived will improve the level of positive emotions and reduce the levels of negative emotions while partaking in the recreational activity. The study advocates that video game companies should design more exciting games that offer fun and entertainment to consumers to help improve the psychological well-being of consumers during this crisis.

The last strand of studies in this cluster emphasizes the role of media in overcoming the performance impact of COVID-19 across industries. These studies highlight the significance of the media in enhancing performance of retail supply chains, tourism, and brand engagement (e.g., [ 83 , 84 , 85 ]. For example, Im et al. [ 84 ] develop two joint models with fixed-effects estimations to examine the relationships among the pandemic, online information search, social distancing, and firm performance in the tourism and hospitality industries. The first model explored the relationship among COVID-19, information search, social distancing and stock performance of tourism and hospitality companies. The results reveal that news coverage on COVID-19 significantly impacts information search and social distancing, and social distancing, in turn, exerts an impact on stock performance. The second analysis focused on the effect of the pandemic on hotel reviews through information search and social distancing for tourist attractions at the regional level. The results indicate that when looking at the geographical effect, news coverage and the number of confirmed cases both lead to variations in social distancing and information search for tourist attractions and these behavioral tendencies are influential in hotel selection. Thus, media coverage and the number of confirmed cases in the news significantly influence social distancing actions of consumers which in turn influences the stock performance of tourism and hospitality industries.

Cluster 3: performance outcomes and strategies

The final cluster contains a number of articles which discuss recorded or projected effects of COVID-19 on various business setups while offering specific remedies, and perspectives for sustained resilience and stronger performance. In Ivanov [ 51 ] the impact of COVID-19 on global supply chains is examined using a simulation-based methodology to predict and examine disruption effects on supply chain performance. This groundbreaking study sets the basis for later empirical studies on supply chains resilience and performance in pandemic era. The study primarily analyzes the manner in which simulation-based methodology can be adopted to examine and predict the effect of pandemic on global supply chain performance. The study highlights the need for firms to be resilient against the disruptions, risks and uncertainties caused by the COVID-19 as such epidemics start small but scale fast and spread across vast geographical expanses creating uncertainty and resulting in numerous unknown and usually adverse outcomes. The results of the simulation based on primary and secondary data offers possibility of predicting both long-term and short-term supply chains performance impact of pandemic in different scenarios. The study approach helps to identify the successful and wrong elements of risk preparedness/mitigation and recovery policies when pandemics erupt. The study results indicate that the timing of the opening and closing of facilities at different strata may become a key factor that determines the impact of epidemic outbreak on supply chain performance rather than the speed of epidemic spread or the duration of an upstream disruption. That is, in the event of pandemic propagation, supply chain performance and reaction depend largely on the timing and the scale of disruption propagation and the sequence of facility opening and closing at various supply chains strata.

Ratten [ 60 ] reviews literature on crisis and its effects on entrepreneurship. By focusing more intently on cultural, lifestyle and social changes experienced in society, the study examines how entrepreneurship has changed in the wake of COVID-19. The study highlights the need for stakeholders to be proactive during a crisis, as it presents both an opportunity and a threat. The study advocates for entrepreneurs to build a social movement by considering broader community needs in addition to their business needs. Additionally, the study highlights the need for a focus on societal trends as well as social changes as a way to surmount possible business setbacks as a result of COVID-19, while taking full advantage of new opportunities. By focusing on inter-organizational networks and collaboration, the study posits that entrepreneurs will begin to leverage and create new, relevant and sustainable innovations. Through appropriate information and resource sharing policy can ensure that entrepreneurs are equipped with relevant tools to rejuvenate troubled industries and grow related businesses.

Ratten [ 62 ] examines the extent to which COVID-19 has influenced sports entrepreneurship, creating the need for considering new business models and encouraging creativity. The study examines the intersection between crisis management and sport entrepreneurship and provides concrete paths to resilience, growth and performance success in dealing with COVID-19. The study examines the concept of sports entrepreneurship, and highlights the critical role technological innovation has played in the success of businesses in this domain. Although the health crisis caused by COVID-19 has resulted in the postponement and cancelation of various sports games including Euro 2020, and the Olympics among others, the study suggests that firms may rise above these setbacks with investments in capital and infrastructure to encourage greater customer and fan engagement as a way to be more entrepreneurially oriented.

Ratten [ 61 ] considers the extent to which COVID-19 has influenced educational entrepreneurship in its almost complete shift to online learning. The global education system was profoundly affected in areas of service, research and teaching. However, the study proffers that educational innovation and the leveraging of multifaceted and rich digital learning environments provides sustainable means through which education communities may cope with the devastating effects of COVID-19. By reimagining online teaching and learning experiences, possibly including artificial intelligence-related tools, and adopting a complimentary approach of innovation and empathy, education communities will begin to identify new revenue streams while strengthening existing ones.

Next, we discuss seminal studies that focus on how entities are responding to the impacts of the pandemic for survival and sustainability (e.g., [ 63 , 86 , 87 , 88 ]). Santos et al. [ 63 ] undertake a comparative study across nine countries to unravel the factors that affect COVID-19 infections and deaths across countries to sustain economies. The study specifically looks at socio-economic indicators and COVOD-19 testing, comparison of infection and death rates across countries and the impact of climate on infection rates across countries. The study finds a significant impact of climate change on COVID-19 infection rate. The results also indicate that socio-economic indicators such as security index, innovation, and GDP per capita are important for a country's sustainability, being imperative to respond to anxious moments such as what nations are living from the COVID-19 pandemic. Through content analysis, Hossain [ 89 ] investigates how the sharing economy (SE) is coping with the changing environment triggered by the Covid-19. The study examines SE sector from four main perspectives: service providers, SE firms, regulatory bodies, and service receivers (customers). The study also explores SE along the following themes: income reduction, anxiety, job loss, hygiene and safety, cancelation, overcoming strategy, and outcomes. The study results indicate the devastating impact of the pandemic on the performance of SE firms and service providers such as Airbnb, accommodation hosts, Uber, and their Uber drivers. Therefore, firms and service providers have adopted strategies to survive in business. The study indicates that because of the pandemic, accommodation hosts are looking at long-term tenants and focusing on domestic instead of foreign guests. This is mirrored by Airbnb strategy of beginning to focus more on long-term stays. These overcoming strategies significantly reduce the impact of the pandemic on SE performance.

Directions for future research

Following the critical review of several relevant studies, this study seeks to categorize and highlight important gaps in literature as well as pertinent trends and foci which research may benefit from while offering practical knowledge and solutions for policy and practice. By structuring relevant gaps and research trends into unique categories, the study provides a means to decompose the broad research domain into vital and unique sub-domains, each warranting extensive and in-depth consideration. For instance, the cluster analysis reveals unexplored areas such as the use of digital technologies and big data to boost performance in the era of viral pandemic. For methodological gaps, the studies analyzed are limited in terms of collecting data at the early stages of the pandemic, hence, the need to consider longitudinal data to better understand the performance impact of pandemic. Another methodological gap is that most of the studies use case-study approach. For contextual gap, the cluster analysis shows lack of attention to supply chains reactions under different pandemic plans. The supply chain performance studies also omitted elements such as back-up suppliers, reserved capacities, regional sub-contracting and lead-time reservations, which could obscure managerial insight. The studies are also limited to upstream disruptions, which call for examination of pandemic disruption in downstream supply chains strata and the antecedent impact on forward and backward propagations of ripple effect.

To facilitate research in addressing the theoretical, contextual, and methodological gaps such as those highlighted, we implement a four-step approach to discern future research agenda by adopting content and bibliometric analyses (Bahoo, 89 ). First, we reviewed 50 top-cited articles that make a citation map. Second, we reviewed all the influential and trending articles during the last 6 months (January to July 2021). Third, we reviewed the remaining articles in our sample to circumvent top citation bias. Fourth, we transformed the possible research agenda into research questions and excluded those questions already investigated by researchers. This systematic process produced the 20 future research questions listed in Table 9 . Through an in-depth qualitative and quantitative review, we recommend a need to establish an appropriate pandemic response framework to help businesses, governments and policy makers to maintain resilience besides maintaining public health safety during such crises.

Conclusion and limitations

The study provided an integrative review to map out the COVID-19 performance discourse. The study adopts a systematic approach to identifying relevant literature used in this review. The study performs a performance analysis to identify seminal studies, impactful authors and high-ranking journals and affiliations. Additionally, the study provides a geographical science mapping of research attention. To guide future research direction, the study conducts a keyword analysis and cluster analysis to identify relevant research themes.

Although the study makes relevant contribution to knowledge, and highlights impactful scholars in the field, the list of impactful scholars provided in this article is far from exhaustive. Additionally, the study adopts a combination of the H, G and M indexes as these indices provide some idea of the impact of authors, it must be noted that such analyses are not without fault. As such, future studies may adopt a wider combination of robust indices in assessing performance factors of authors, articles and journals. The research on the impact of covid-19 on firm performance is now developing so the review in this study focused on all firms. Thus, future reviews may look at how the pandemic affects performance of specific firms and/or sectors since different firms may have different growth objectives, which could influence the impact of COVID-19 on these firms.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author.

Abbreviations

Coronavirus disease

United States of America

United Kingdom

Service mega-disruptions

Resource-challenges equilibrium

Information and communication technology

Sharing economy

Gross domestic product

Morens DM, Taubenberger JK, Folkers GK, Fauci AS (2009) An historical antecedent of modern guidelines for community pandemic influenza mitigation. Public Health Rep 124(1):22–25. https://doi.org/10.1177/003335490912400105

Article   Google Scholar  

Goldin I, Mariathasan M (2015) The butterfly defect. Princeton University Press, Princeton. https://doi.org/10.1515/9781400850204/PDF

Book   Google Scholar  

Qiu J, Wang X, He S, Liu H, Lai J, Wang L (2017) The catastrophic landside in Maoxian County, Sichuan, SW China, on June 24, 2017. Nat Hazards 89(3):1485–1493. https://doi.org/10.1007/S11069-017-3026-9

Wenzel M, Stanske S, Lieberman MB (2021) Strategic responses to crisis. Strateg Manag J 42(2):O16–O27. https://doi.org/10.1002/smj.3161

Balkhair AA (2020) COVID-19 pandemic: a new chapter in the history of infectious diseases. Oman Med J 35(2):e123. https://doi.org/10.5001/OMJ.2020.41

Chakraborty T, Kumar A, Upadhyay P, Dwivedi YK (2021) Link between social distancing, cognitive dissonance, and social networking site usage intensity: a country-level study during the COVID-19 outbreak. Internet Res 31(2):419–456. https://doi.org/10.1108/INTR-05-2020-0281/FULL/HTML

Chiappini H, Vento G, De Palma L (2021) The impact of COVID-19 lockdowns on sustainable indexes. Sustainability 13(4):1846

Eggers F (2020) Masters of disasters? Challenges and opportunities for SMEs in times of crisis. J Bus Res 116:199–208. https://doi.org/10.1016/J.JBUSRES.2020.05.025

Almeida V, Barrios S, Christl M, De Poli S, Tumino A, van der Wielen W (2021) The impact of COVID-19 on households´ income in the EU. J Econ Inequal 19(3):413–431. https://doi.org/10.1007/S10888-021-09485-8

Le XTT, Dang AK, Toweh J, Nguyen QN, Le HT, Do TTT, Phan HBT, Nguyen TT, Pham QT, Ta NKT, Nguyen QT, Nguyen AN, Van Duong Q, Hoang MT, Pham HQ, Vu LG, Tran BX, Latkin CA, Ho CSH, Ho RCM (2020) Evaluating the psychological impacts related to COVID-19 of Vietnamese people under the first nationwide partial lockdown in Vietnam. Front Psychiatry. https://doi.org/10.3389/FPSYT.2020.00824/PDF

Carlsson-Szlezak P, Reeves M, Swartz P (2020) What coronavirus could mean for the global economy. Harv Bus Rev 3(10):1–10

Google Scholar  

Griffiths S, Furszyfer Del Rio D, Sovacool B (2021) Policy mixes to achieve sustainable mobility after the COVID-19 crisis. Renew Sustain Energy Rev 143:110919. https://doi.org/10.1016/J.RSER.2021.110919

Papadopoulos T, Baltas KN, Balta ME (2020) The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. Int J Inf Manag 55:102192. https://doi.org/10.1016/j.ijinfomgt.2020.102192

Aidoo SO, Agyapong A, Acquaah M, Akomea SY (2021) The performance implications of strategic responses of SMEs to the covid-19 pandemic: Evidence from an African economy. Afr J Manag 7(1):74–103. https://doi.org/10.1080/23322373.2021.1878810

Amankwah-Amoah J, Khan Z, Wood G (2021) COVID-19 and business failures: the paradoxes of experience, scale, and scope for theory and practice. Eur Manag J 39(2):179–184. https://doi.org/10.1016/J.EMJ.2020.09.002

Hu S, Zhang Y (2021) COVID-19 pandemic and firm performance: cross-country evidence. Int Rev Econ Finance 74:365–372. https://doi.org/10.1016/J.IREF.2021.03.016

Amankwah-Amoah J (2020) Stepping up and stepping out of COVID-19: New challenges for environmental sustainability policies in the global airline industry. J Clean Prod 271:123000. https://doi.org/10.1016/J.JCLEPRO.2020.123000

Grover A, Karplus VJ (2021) Coping with COVID-19. World Bank, Washington. https://doi.org/10.1596/1813-9450-9514

Kaushal V, Srivastava S (2021) Hospitality and tourism industry amid COVID-19 pandemic: perspectives on challenges and learnings from India. Int J Hosp Manag 92:102707. https://doi.org/10.1016/J.IJHM.2020.102707

Guan D, Wang D, Hallegatte S, Davis SJ, Huo J, Li S, Bai Y, Lei T, Xue Q, Coffman DM, Cheng D, Chen P, Liang X, Xu B, Lu X, Wang S, Hubacek K, Gong P (2020) Global supply-chain effects of COVID-19 control measures. Nat Hum Behav 4(6):577–587. https://doi.org/10.1038/s41562-020-0896-8

Ramelli S, Wagner AF (2020) Feverish stock price reactions to COVID-19. Rev Corp Finance Stud 9(3):622–655. https://doi.org/10.1093/RCFS/CFAA012

Smales LA (2020) Investor attention and the response of US stock market sectors to the COVID-19 crisis. https://doi.org/10.1108/RBF-06-2020-0138

Gourinchas PO, Kalemli-Özcan Ṣ, Penciakova V, Sander N (2020) COVID-19 and SME failures. Cambridge, MA. https://doi.org/10.3386/W27877

Kraus S, Clauss T, Breier M, Gast J, Zardini A, Tiberius V (2020) The economics of COVID-19: initial empirical evidence on how family firms in five European countries cope with the corona crisis. Int J Entrep Behav Res 26(5):1067–1092. https://doi.org/10.1108/IJEBR-04-2020-0214

Carracedo P, Puertas R, Marti L (2021) Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis. J Bus Res 132(November 2020):586–593. https://doi.org/10.1016/j.jbusres.2020.11.043

Gaziulusoy AI, Boyle C (2013) Proposing a heuristic reflective tool for reviewing literature in transdisciplinary research for sustainability. J Clean Prod 48:139–147. https://doi.org/10.1016/J.JCLEPRO.2012.04.013

Dabić M, Maley J, Dana LP, Novak I, Pellegrini MM, Caputo A (2020) Pathways of SME internationalization: a bibliometric and systematic review. Small Bus Econ 55(3):705–725. https://doi.org/10.1007/S11187-019-00181-6

Pizzi S, Caputo A, Corvino A, Venturelli A (2020) Management research and the UN sustainable development goals (SDGs): a bibliometric investigation and systematic review. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.124033

Aria M, Cuccurullo C (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informet 11(4):959–975. https://doi.org/10.1016/j.joi.2017.08.007

Zupic I, Čater T (2015) Bibliometric methods in management and organization. Organ Res Methods 18(3):429–472. https://doi.org/10.1177/1094428114562629

Caputo A, Marzi G, Pellegrini MM, Rialti R (2018) Conflict management in family businesses: a bibliometric analysis and systematic literature review. Int J Confl Manag 29(4):519–542. https://doi.org/10.1108/IJCMA-02-2018-0027/FULL/HTML

Carpenter CR, Cone DC, Sarli CC (2014) Using publication metrics to highlight academic productivity and research impact. Acad Emerg Med 21(10):1160–1172. https://doi.org/10.1111/acem.12482

Costas R, Bordons M (2008) Is g-index better than h-index? An exploratory study at the individual level. Scientometrics 77(2):267–288. https://doi.org/10.1007/s11192-007-1997-0

Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.0507655102

Egghe L (2006) Theory and practise of the g-index. Scientometrics 69(1):131–152. https://doi.org/10.1007/s11192-006-0144-7

Costas R, Franssen T (2018) Reflections around “the cautionary use” of the h-index: response to Teixeira da Silva and Dobránszki. Scientometrics 115(2):1125–1130. https://doi.org/10.1007/S11192-018-2683-0

Mackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, Spencer H, Rusek M, Anderson J, Veazie S, Smith M, Kansagara D (2021) Racial and ethnic disparities in covid-19-related infections, hospitalizations, and deaths a systematic review. Ann Intern Med 174(3):362–373. https://doi.org/10.7326/M20-6306/SUPPL_FILE/M20-6306_SUPPLEMENT.PDF

Kerber G (2020) Everything is interrelated. Ecum Rev 72(4):596–608. https://doi.org/10.1111/EREV.12549

Golan MS, Jernegan LH, Linkov I (2020) Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic. Environ Syst Decis 40(2):222–243. https://doi.org/10.1007/S10669-020-09777-W

Mollenkopf DA, Ozanne LK, Stolze HJ (2021) A transformative supply chain response to COVID-19. J Serv Manag 32(2):190–202. https://doi.org/10.1108/JOSM-05-2020-0143/FULL/PDF

Škare M, Soriano DR, Porada-Rochoń M (2021) Impact of COVID-19 on the travel and tourism industry. Technol Forecast Soc Change 163:120469. https://doi.org/10.1016/J.TECHFORE.2020.120469

Juergensen J, Guimón J, Narula R (2020) European SMEs amidst the COVID-19 crisis: assessing impact and policy responses. J Ind Bus Econ 47(3):499–510. https://doi.org/10.1007/s40812-020-00169-4

Kuckertz A, Brändle L, Gaudig A, Hinderer S, Reyes CA, Prochotta A, Steinbrink KM, Berger ES (2020) Startups in times of crisis—a rapid response to the COVID-19 pandemic. J Bus Ventur Insights 13:e00169. https://doi.org/10.1016/j.jbvi.2020.e00169

Finsterwalder J, Kuppelwieser VG (2020) Equilibrating resources and challenges during crises: a framework for service ecosystem well-being. J Serv Manag 31(6):1107–1129. https://doi.org/10.1108/JOSM-06-2020-0201/FULL/

Bhaduri RM (2019) Leveraging culture and leadership in crisis management. Eur J Train Dev 43(5–6):554–569. https://doi.org/10.1108/EJTD-10-2018-0109/FULL/HTML

Janis IL (1989) Crucial Decisions: Leadership in Policymaking and Crisis Management. Free Press, New York

Gupta SD (2020) Ravaging pandemic of COVID-19. J Health Manag 22(2):115–116. https://doi.org/10.1177/0972063420951876

Gebhard C, Regitz-Zagrosek V, Neuhauser HK, Morgan R, Klein SL (2020) Impact of sex and gender on COVID-19 outcomes in Europe. Biol Sex Differ. https://doi.org/10.1186/S13293-020-00304-9

Alon T, Doepke M, Olmstead-Rumsey J, Tertilt M (2020) The impact of COVID-19 on gender equality. SSRN. https://doi.org/10.3386/W26947

Chen X, Chen J, Wu D, Xie Y, Li J (2016) Mapping the research trends by co-word analysis based on keywords from funded project. Procedia Comput Sci 91:547–555. https://doi.org/10.1016/J.PROCS.2016.07.140

Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak. Transp Res Part E 136(March):101922. https://doi.org/10.1016/j.tre.2020.101922

Dryhurst S, Schneider CR, Kerr J, Freeman AL, Recchia G, Van Der Bles AM, Spiegelhalter D, Van Der Linden S (2020) Risk perceptions of COVID-19 around the world. J Risk Res 23(7–8):994–1006. https://doi.org/10.1080/13669877.2020.1758193

Sigala M (2020) Tourism and COVID-19: impacts and implications for advancing and resetting industry and research. J Bus Res 117:312–321. https://doi.org/10.1016/J.JBUSRES.2020.06.015

Jiang Y, Wen J (2020) Effects of COVID-19 on hotel marketing and management: a perspective article. Int J Contemp Hosp Manag 32(8):2563–2573. https://doi.org/10.1108/IJCHM-03-2020-0237/FULL/HTML

Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity invariance-based structural equation modeling. J Acad Mark Sci 43(1):115–135

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioralresearch: a critical review of the literature and recommended remedies. J Appl psychol 88(5):879

Hall MC, Prayag G, Fieger P, Dyason D (2020) Beyond panic buying: consumption displacement and COVID-19. J Serv Manage 32(1):

Zhang D, Hu M, Ji Q (2020) Financial markets under the global pandemic of COVID-19. Financ Res Lett 36:101528

Narayan PK, Phan DHB, Liu G (2021) COVID-19 lockdowns, stimulus packages, travel bans, and stock returns. Financ Res Lett 38:101732

Ratten V (2020) Coronavirus (Covid-19) and entrepreneurship: cultural, lifestyle and societal changes. J Entrep Emerg Econ 13(4):747–761. https://doi.org/10.1108/JEEE-06-2020-0163/FULL/PDF

Ratten V (2020) Coronavirus (Covid-19) and the entrepreneurship education community. J Enterp Communities 14(5):753–764. https://doi.org/10.1108/JEC-06-2020-0121/FULL/PDF

Ratten V (2020) Coronavirus disease (COVID-19) and sport entrepreneurship. Int J Entrep Behav Res 26(6):1379–1388. https://doi.org/10.1108/IJEBR-06-2020-0387/FULL/PDF

Santos E, Oliveira M, Ratten V, Tavares FO, Tavares VC (2021) A reflection on explanatory factors for COVID-19: a comparative study between countries. Thunderbird Int Bus Rev 63(3):285–301. https://doi.org/10.1002/tie.22188

Tavares F, Santos E, Diogo, A, Ratten V (2020b) An analysis of the experiences based on experimental marketing: pandemic COVID-19 quarantine. World J Entrep Manage Sustain Dev 16(4):327–340. https://doi.org/10.1108/WJEMSD-08-2020-0098 .

Asian S, Wang J, Dickson G (2020) Trade disruptions, behavioral biases, and social influences: Can luxury sporting goods supply chains be immunized? Transportation Research Part E: Logistics and Transportation Review 143:102064.

Kells S (2020) Impacts of COVID-19 on corporate governance and assurance, international finance and economics, and non-fiction book publishing: some personal reflections. J Acc Org Chang Vol. 16:(4):629–635. https://doi.org/10.1108/JAOC-08-2020-0115 .

Tavares F, Santos E, Diogo A, Ratten V (2020a) An analysis of the experiences based on experimental marketing: pandemic COVID-19 quarantine. World J Entrep Manage Sustain Dev 16(4):327–340

Ferreira FAF (2018) Mapping the field of arts-based management: bibliographic coupling and co-citation analyses. J Bus Res 85:348e357. https://doi.org/10.1016/j.jbusres.2017.03.026 .

Braun V, Clarke V (2012) Thematic analysis. In: Cooper HE, Camic PM, Long DL, Panter AT, Rindskopf DE, Sher KJ (eds) APA handbook of research methods in psychology, Vol 2: research designs: quantitative, qualitative, neuropsychological, and biological. American Psychological Association, Washington

Andrew J, Baker M, Guthrie J, Martin-Sardesai A (2020) Australia’s COVID-19 public budgeting response: the straitjacket of neoliberalism. J Public Budg Account Financ Manag 32(5):759–770

Baum T, Hai NTT (2020) Hospitality, tourism, human rights and the impact of COVID-19. Int J Contemp Hosp Manag 32(7):2397–2407. https://doi.org/10.1108/IJCHM-03-2020-0242/FULL/HTML

Kabadayi S, O’Connor GE, Tuzovic S (2020) Viewpoint: the impact of coronavirus on service ecosystems as service mega-disruptions. J Serv Mark 34(6):809–817. https://doi.org/10.1108/JSM-03-2020-0090/FULL/HTML

Ivanov D, Dolgui A (2021) OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic : managerial insights and research implications. Int J Prod Econ 232(May 2020):107921. https://doi.org/10.1016/j.ijpe.2020.107921

Baudier P, Kondrateva G, Ammi C, Chang V, Schiavone F (2021) Technological Forecasting & Social Change Patients’ perceptions of teleconsultation during COVID-19: a cross-national study. Technol Forecast Soc Chang 163(December 2020):120510. https://doi.org/10.1016/j.techfore.2020.120510

Brodie RJ, Ranjan KR, Verreynne ML, Jiang Y, Previte J (2021) Coronavirus crisis and health care: learning from a service ecosystem perspective. J Serv Theory Pract 31(2):225–246. https://doi.org/10.1108/JSTP-07-2020-0178

Gössling S, Scott D, Hall CM (2020) Pandemics, tourism and global change: a rapid assessment of COVID-19. J Sustain Tour. https://doi.org/10.1080/09669582.2020.1758708

Giousmpasoglou C, Marinakou E, Zopiatis A (2021) Hospitality managers in turbulent times: the COVID-19 crisis. Int J Contemp Hosp Manag 33(4):1297–1318. https://doi.org/10.1108/IJCHM-07-2020-0741/FULL/PDF

Carnevale JB, Hatak I (2020) Employee adjustment and well-being in the era of COVID-19: implications for human resource management. J Bus Res 116:183–187. https://doi.org/10.1016/J.JBUSRES.2020.05.037

Viola C, Toma P, Manta F, Benvenuto M (2021) The more you know, the better you act? Institutional communication in Covid-19 crisis management. Technol Forecast Soc Change. https://doi.org/10.1016/J.TECHFORE.2021.120929

Machmud M, Irawan B, Karinda K, Susilo J (2021) Analysis of the intensity of communication and coordination of government officials on twitter social media during the Covid-19 handling in Indonesia. Acad J Interdiscip Stud 10(3):319–319

Mohammed A, Ferraris A (2021) Factors influencing user participation in social media: evidence from twitter usage during COVID-19 pandemic in Saudi Arabia. Technol Soc. https://doi.org/10.1016/J.TECHSOC.2021.101651

Kim M (2021) Does playing a video game really result in improvements in psychological well-being in the era of COVID-19? J Retail Consum Serv. https://doi.org/10.1016/J.JRETCONSER.2021.102577

Hollebeek LD, Smith DL, Kasabov E, Hammedi W, Warlow A, Clark MK (2021) Customer brand engagement during service lockdown. J Serv Mark 35(2):201–209. https://doi.org/10.1108/JSM-05-2020-0199/FULL/HTML

Im J, Kim J, Choeh JY (2021) COVID-19, social distancing, and risk-averse actions of hospitality and tourism consumers: a case of South Korea. J Destin Mark Manag. https://doi.org/10.1016/J.JDMM.2021.100566

Sharma M, Luthra S, Joshi S, Kumar A (2021) Accelerating retail supply chain performance against pandemic disruption: adopting resilient strategies to mitigate the long-term effects. J Enterp Inf Manag 34(6):1844–1873. https://doi.org/10.1108/JEIM-07-2020-0286/FULL/HTML

Hossain M (2021) The effect of the Covid-19 on sharing economy activities. J Clean Prod 280:124782. https://doi.org/10.1016/j.jclepro.2020.124782

Tuzovic S, Kabadayi S (2021) The influence of social distancing on employee well-being: a conceptual framework and research agenda. J Serv Manag 32(2):145–160. https://doi.org/10.1108/JOSM-05-2020-0140

Williams CC (2020) The coronavirus pandemic, short-term employment support schemes and undeclared work : some lessons from Europe. https://doi.org/10.1108/ER-05-2020-0218

Bahoo, S. (2020). Corruption in banks: A bibliometric review and agenda. Finance Research Letters, 35, 101499.

Jamal, T., & Budke, C. (2020). Tourism in a world with pandemics: local-global responsibility and action. Journal of tourism futures, 6(2), 181-188.

Lai, I. K. W., & Wong, J. W. C. (2020). Comparing crisis management practices in the hotel industry between initial and pandemic stages of COVID-19. International Journal of Contemporary Hospitality Management.

Jones, P., & Comfort, D. (2020). The COVID-19 crisis and sustainability in the hospitality industry. International journal of contemporary hospitality management, Volume 32 Issue 10.

Ahrens T, Ferry L (2020) Financial resilience of English local government in the aftermath of COVID-19. Journal of Public Budgeting, Accounting & Financial Management, Volume 32 Issue 5.

Download references

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

School of Business, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Kingsley Opoku Appiah

School of Economics and Management, Changsha University of Science and Technology, Changsha, China

Bismark Addai

Department of Accounting, FSI Chartered Accountants and Advisory, Accra, Ghana

Wesley Ekuban

Department of Public Leadership and Social Enterprise (PuLSE), The Open University, Milton Keynes, UK

Suzzie Owiredua Aidoo

Kent Business School, University of Kent, Kent, UK

Joseph Amankwah-Amoah

You can also search for this author in PubMed   Google Scholar

Contributions

KOA conceived the idea, KOA and WE performed the literature search; KOA, BA, and SOA performed the analysis; KOA, BA and WE wrote the first draft, JA-A, BA, KOA and SOA critically reviewed the manuscript; KOA, BA, and SOA revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bismark Addai .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Appiah, K.O., Addai, B., Ekuban, W. et al. Management research and the impact of COVID-19 on performance: a bibliometric review and suggestions for future research. Futur Bus J 8 , 41 (2022). https://doi.org/10.1186/s43093-022-00149-1

Download citation

Received : 07 June 2022

Accepted : 28 August 2022

Published : 30 September 2022

DOI : https://doi.org/10.1186/s43093-022-00149-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Management research
• Bibliometrics
• Coronavirus

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study

Contributed equally to this work with: Dominika Maison, Diana Jaworska, Dominika Adamczyk, Daria Affeltowicz

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Faculty of Psychology, University of Warsaw, Warsaw, Poland

Roles Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Roles Conceptualization, Formal analysis, Investigation, Methodology

• Dominika Maison, 
• Diana Jaworska, 
• Dominika Adamczyk, 
• Daria Affeltowicz

• Published: October 11, 2021
• https://doi.org/10.1371/journal.pone.0258133
• Peer Review
• Reader Comments

The conducted qualitative research was aimed at capturing the biggest challenges related to the beginning of the COVID-19 pandemic. The interviews were carried out in March-June (five stages of the research) and in October (the 6 th stage of the research). A total of 115 in-depth individual interviews were conducted online with 20 respondents, in 6 stages. The results of the analysis showed that for all respondents the greatest challenges and the source of the greatest suffering were: a) limitation of direct contact with people; b) restrictions on movement and travel; c) necessary changes in active lifestyle; d) boredom and monotony; and e) uncertainty about the future.

Citation: Maison D, Jaworska D, Adamczyk D, Affeltowicz D (2021) The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study. PLoS ONE 16(10): e0258133. https://doi.org/10.1371/journal.pone.0258133

Editor: Shah Md Atiqul Haq, Shahjalal University of Science and Technology, BANGLADESH

Received: April 6, 2021; Accepted: September 18, 2021; Published: October 11, 2021

Copyright: © 2021 Maison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files ( S1 Dataset ).

Funding: This work was supported by the Faculty of Psychology, University of Warsaw, Poland from the funds awarded by the Ministry of Science and Higher Education in the form of a subsidy for the maintenance and development of research potential in 2020 (501-D125-01-1250000). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The coronavirus disease (COVID-19), discovered in December 2019 in China, has reached the level of a pandemic and, till June 2021, it has affected more than 171 million people worldwide and caused more than 3.5 million deaths all over the world [ 1 ]. The COVID-19 pandemic as a major health crisis has caught the attention of many researchers, which has led to the creation of a broad quantitative picture of human behavior during the coronavirus outbreak [ 2 – 4 ]. What has been established so far is, among others, the psychological symptoms that can occur as a result of lockdown [ 2 ], and the most common coping strategies [ 5 ]. However, what we still miss is an in-depth understanding of the changes in the ways of coping with challenges over different stages of the pandemic. In the following study, we used a longitudinal qualitative method to investigate the challenges during the different waves of the coronavirus pandemic as well as the coping mechanisms accompanying them.

In Poland, the first patient was diagnosed with COVID-19 on the 4 th March 2020. Since then, the number of confirmed cases has grown to more than 2.8 million and the number of deaths to more than 73,000 (June 2021) [ 1 ]. From mid-March 2020, the Polish government, similarly to many other countries, began to introduce a number of restrictions to limit the spread of the virus. These restrictions had been changing from week to week, causing diverse reactions in people [ 6 ]. It needs to be noted that the reactions to such a dynamic situation cannot be covered by a single study. Therefore, in our study we used qualitative longitudinal research in order to monitor changes in people’s emotions, attitudes, and behavior. So far, few longitudinal studies have been carried out that investigated the various issues related to the COVID-19 pandemic; however, all of them were quantitative [ 7 – 10 ]. The qualitative approach (and especially the use of enabling and projective techniques) allows for an in-depth exploration of respondents’ reactions that goes beyond respondents’ declarations and captures what they are less aware of or even unconscious of. This study consisted of six stages of interviews that were conducted at key moments for the development of the pandemic situation in Poland. The first stage of the study was carried out at the moment of the most severe lockdown and the biggest restrictions (March 2020) and was focused on exploration how did people react to the new uncertain situation. The second stage of the study was conducted at the time when restrictions were extended and the obligation to cover the mouth and nose everywhere outside the household were introduced (middle of April 2020) and was focused at the way how did people deal with the lack of family gatherings over Easter. The third stage of the study was conducted at the moment of announcing the four stages of lifting the restrictions (April 2020) and was focused on people’s reaction to an emerging vision of getting back to normalcy. The fourth stage of the study was carried out, after the introduction of the second stage of lifting the restrictions: shopping malls, hotels, and cultural institutions were gradually being opened (May 2020). The fifth stage of the study was conducted after all four stages of restriction lifting were in place (June 2020). Only the obligation to cover the mouth and nose in public spaces, an order to maintain social distance, as well as the functioning of public places under a sanitary regime were still in effect. During those 5 stages coping strategies with the changes in restrictions were explored. The sixth and last stage of the study was a return to the respondents after a longer break, at the turn of October and November 2020, when the number of coronavirus cases in Poland began to increase rapidly and the media declared “the second wave of the pandemic”. It was the moment when the restrictions were gradually being reintroduced. A full description of the changes occurring in Poland at the time of the study can be found in S1 Table .

The following study is the first qualitative longitudinal study investigating how people cope with the challenges arising from the COVID-19 pandemic at its different stages. The study, although conducted in Poland, shows the universal psychological relations between the challenges posed by the pandemic (and, even more, the restrictions resulting from the pandemic, which were very similar across different countries, not only European) and the ways of dealing with them.

Literature review

The COVID-19 pandemic has led to a global health crisis with severe economic [ 11 ], social [ 3 ], and psychological consequences [ 4 ]. Despite the fact that there were multiple crises in recent years, such as natural disasters, economic crises, and even epidemics, the coronavirus pandemic is the first in 100 years to severely affect the entire world. The economic effects of the COVID-19 pandemic concern an impending global recession caused by the lockdown of non-essential industries and the disruption of production and supply chains [ 11 ]. Social consequences may be visible in many areas, such as the rise in family violence [ 3 ], the ineffectiveness of remote education, and increased food insecurity among impoverished families due to school closures [ 12 ]. According to some experts, the psychological consequences of COVID-19 are the ones that may persist for the longest and lead to a global mental health crisis [ 13 ]. The coronavirus outbreak is generating increased depressive symptoms, stress, anxiety, insomnia, denial, fear, and anger all over the world [ 2 , 14 ]. The economic, social, and psychological problems that people are currently facing are the consequences of novel challenges that have been posed by the pandemic.

The coronavirus outbreak is a novel, uncharted situation that has shaken the world and completely changed the everyday lives of many individuals. Due to the social distancing policy, many people have switched to remote work—in Poland, almost 75% of white-collar workers were fully or partially working from home from mid-March until the end of May 2020 [ 15 ]. School closures and remote learning imposed a new obligation on parents of supervising education, especially with younger children [ 16 ]. What is more, the government order of self-isolation forced people to spend almost all their time at home and limit or completely abandon human encounters. In addition, the deteriorating economic situation was the cause of financial hardship for many people. All these difficulties and challenges arose in the aura of a new, contagious disease with unexplored, long-lasting health effects and not fully known infectivity and lethality [ 17 ]. Dealing with the situation was not facilitated by the phenomenon of global misinformation, called by some experts as the “infodemic”, which may be defined as an overabundance of information that makes it difficult for people to find trustworthy sources and reliable guidance [ 18 ]. Studies have shown that people have multiple ways of reacting to a crisis: from radical and even violent practices, towards individual solutions and depression [ 19 ]. Not only the challenges arising from the COVID-19 pandemic but also the ways of reacting to it and coping with it are issues of paramount importance that are worth investigating.

The reactions to unusual crisis situations may be dependent on dispositional factors, such as trait anxiety or perceived control [ 20 , 21 ]. A study on reactions to Hurricane Hugo has shown that people with higher trait anxiety are more likely to develop posttraumatic symptoms following a natural disaster [ 20 ]. Moreover, lack of perceived control was shown to be positively related to the level of distress during an earthquake in Turkey [ 21 ]. According to some researchers, the COVID-19 crisis and natural disasters have much in common, as the emotions and behavior they cause are based on the same primal human emotion—fear [ 22 ]. Both pandemics and natural disasters disrupt people’s everyday lives and may have severe economic, social and psychological consequences [ 23 ]. However, despite many similarities to natural disasters, COVID-19 is a unique situation—only in 2020, the current pandemic has taken more lives than the world’s combined natural disasters in any of the past twenty years [ 24 ]. It needs to be noted that natural disasters may pose different challenges than health crises and for this reason, they may provoke disparate reactions [ 25 ]. Research on the reactions to former epidemics has shown that avoidance and safety behaviors, such as avoiding going out, visiting crowded places, and visiting hospitals, are widespread at such times [ 26 ]. When it comes to the ways of dealing with the current COVID-19 pandemic, a substantial part of the quantitative research on this issue focuses on coping mechanisms. Studies have shown that the most prevalent coping strategies are highly problem-focused [ 5 ]. Most people tend to listen to expert advice and behave calmly and appropriately in the face of the coronavirus outbreak [ 5 ]. Problem-focused coping is particularly characteristic of healthcare professionals. A study on Chinese nurses has shown that the closer the problem is to the person and the more fear it evokes, the more problem-focused coping strategy is used to deal with it [ 27 ]. On the other hand, a negative coping style that entails risky or aggressive behaviors, such as drug or alcohol use, is also used to deal with the challenges arising from the COVID-19 pandemic [ 28 ]. The factors that are correlated with negative coping include coronavirus anxiety, impairment, and suicidal ideation [ 28 ]. It is worth emphasizing that social support is a very important component of dealing with crises [ 29 ].

Scientists have attempted to systematize the reactions to difficult and unusual situations. One such concept is the “3 Cs” model created by Reich [ 30 ]. It accounts for the general rules of resilience in situations of stress caused by crises, such as natural disasters. The 3 Cs stand for: control (a belief that personal resources can be accessed to achieve valued goals), coherence (the human desire to make meaning of the world), and connectedness (the need for human contact and support) [ 30 ]. Polizzi and colleagues [ 22 ] reviewed this model from the perspective of the current COVID-19 pandemic. The authors claim that natural disasters and COVID-19 pandemic have much in common and therefore, the principles of resilience in natural disaster situations can also be used in the situation of the current pandemic [ 22 ]. They propose a set of coping behaviors that could be useful in times of the coronavirus outbreak, which include control (e.g., planning activities for each day, getting adequate sleep, limiting exposure to the news, and helping others), coherence (e.g., mindfulness and developing a coherent narrative on the event), and connectedness (e.g., establishing new relationships and caring for existing social bonds) [ 22 ].

Current study

The issue of the challenges arising from the current COVID-19 pandemic and the ways of coping with them is complex and many feelings accompanying these experiences may be unconscious and difficult to verbalize. Therefore, in order to explore and understand it deeply, qualitative methodology was applied. Although there were few qualitative studies on the reaction to the pandemic [e.g., 31 – 33 ], they did not capture the perception of the challenges and their changes that arise as the pandemic develops. Since the situation with the COVID-19 pandemic is very dynamic, the reactions to the various restrictions, orders or bans are evolving. Therefore, it was decided to conduct a qualitative longitudinal study with multiple interviews with the same respondents [ 34 ].

The study investigates the challenges arising from the current pandemic and the way people deal with them. The main aim of the project was to capture people’s reactions to the unusual and unexpected situation of the COVID-19 pandemic. Therefore, the project was largely exploratory in nature. Interviews with the participants at different stages of the epidemic allowed us to see a wide spectrum of problems and ways of dealing with them. The conducted study had three main research questions:

• What are the biggest challenges connected to the COVID-19 pandemic and the resulting restrictions?
• How are people dealing with the pandemic challenges?
• What are the ways of coping with the restrictions resulting from a pandemic change as it continues and develops (perspective of first 6 months)?

The study was approved by the institutional review board of the Faculty of Psychology University of Warsaw, Poland. All participants were provided written and oral information about the study, which included that participation was voluntary, that it was possible to withdraw without any consequences at any time, and the precautions that would be taken to protect data confidentiality. Informed consent was obtained from all participants. To ensure confidentiality, quotes are presented only with gender, age, and family status.

The study was based on qualitative methodology: individual in-depth interviews, s which are the appropriate to approach a new and unknown and multithreaded topic which, at the beginning of 2020, was the COVID-19 pandemic. Due to the need to observe respondents’ reactions to the dynamically changing situation of the COVID-19 pandemic, longitudinal study was used where the moderator met on-line with the same respondent several times, at specific time intervals. A longitudinal study was used to capture the changes in opinions, emotions, and behaviors of the respondents resulting from the changes in the external circumstances (qualitative in-depth interview tracking–[ 34 ]).

The study took place from the end of March to October 2020. Due to the epidemiological situation in the country interviews took place online, using the Google Meets online video platform. The audio was recorded and then transcribed. Before taking part in the project, the respondents were informed about the purpose of the study, its course, and the fact that participation in the project is voluntary, and that they will be able to withdraw from participation at any time. The respondents were not paid for taking part in the project.

Participants.

In total, 115 interviews were conducted with 20 participants (6 interviews with the majority of respondents). Two participants (number 11 and 19, S2 Table ) dropped out of the last two interviews, and one (number 6) dropped out of the last interview. The study was based on a purposive sample and the respondents differed in gender, age, education, family status, and work situation (see S2 Table ). In addition to demographic criteria intended to ensure that the sample was as diverse as possible, an additional criterion was to have a permanent Internet connection and a computer capable of online video interviewing. Study participants were recruited using the snowball method. They were distant acquaintances of acquaintances of individuals involved in the study. None of the moderators knew their interviewees personally.

A total of 10 men and 10 women participated in the study; their age range was: 25–55; the majority had higher education (17 respondents), they were people with different professions and work status, and different family status (singles, couples without children, and families with children). Such diversity of respondents allowed us to obtain information from different life perspectives. A full description of characteristics of study participants can be found in S2 Table .

Each interview took 2 hours on average, which gives around 240 hours of interviews. Subsequent interviews with the same respondents conducted at different intervals resulted from the dynamics of the development of the pandemic and the restrictions introduced in Poland by the government.

The interviews scenario took a semi-structured form. This allowed interviewers freely modify the questions and topics depending on the dynamics of the conversation and adapt the subject matter of the interviews not only to the research purposes but also to the needs of a given respondent. The interview guides were modified from week to week, taking into account the development of the epidemiological situation, while at the same time maintaining certain constant parts that were repeated in each interview. The main parts of the interview topic guide consisted of: (a) experiences from the time of previous interviews: thoughts, feeling, fears, and hopes; (b) everyday life—organization of the day, work, free time, shopping, and eating, etc.; (c) changes—what had changed in the life of the respondent from the time of the last interview; (d) ways of coping with the situation; and (e) media—reception of information appearing in the media. Additionally, in each interview there were specific parts, such as the reactions to the beginning of the pandemic in the first interview or the reaction to the specific restrictions that were introduced.

The interviews were conducted by 5 female interviewers with experience in moderating qualitative interviews, all with a psychological background. After each series of interviews, all the members of the research teams took part in debriefing sessions, which consisted of discussing the information obtained from each respondent, exchanging general conclusions, deciding about the topics for the following interview stage, and adjusting them to the pandemic situation in the country.

Data analysis.

All the interviews were transcribed in Polish by the moderators and then double-checked (each moderator transcribed the interviews of another moderator, and then the interviewer checked the accuracy of the transcription). The whole process of analysis was conducted on the material in Polish (the native language of the authors of the study and respondents). The final page count of the transcript is approximately 1800 pages of text. The results presented below are only a portion of the total data collected during the interviews. While there are about 250 pages of the transcription directly related to the topic of the article, due to the fact that the interview was partly free-form, some themes merge with others and it is not possible to determine the exact number of pages devoted exclusively to analysis related to the topic of the article. Full dataset can be found in S1 Dataset .

Data was then processed into thematic analysis, which is defined as a method of developing qualitative data consisting of the identification, analysis, and description of the thematic areas [ 35 ]. In this type of analysis, a thematic unit is treated as an element related to the research problem that includes an important aspect of data. An important advantage of thematic analysis is its flexibility, which allows for the adoption of the most appropriate research strategy to the phenomenon under analysis. An inductive approach was used to avoid conceptual tunnel vision. Extracting themes from the raw data using an inductive approach precludes the researcher from imposing a predetermined outcome.

As a first step, each moderator reviewed the transcripts of the interviews they had conducted. Each transcript was thematically coded individually from this point during the second and the third reading. In the next step, one of the researchers reviewed the codes extracted by the other members of the research team. Then she made initial interpretations by generating themes that captured the essence of the previously identified codes. The researcher created a list of common themes present in all of the interviews. In the next step, the extracted themes were discussed again with all the moderators conducting the coding in order to achieve consistency. This collaborative process was repeated several times during the analysis. Here, further superordinate (challenges of COVID-19 pandemic) and subordinate (ways of dealing with challenges) themes were created, often by collapsing others together, and each theme listed under a superordinate and subordinate category was checked to ensure they were accurately represented. Through this process of repeated analysis and discussion of emerging themes, it was possible to agree on the final themes that are described below.

Main challenges of the COVID-19 pandemic.

Challenge 1 –limitation of direct contact with people . The first major challenge of the pandemic was that direct contact with other people was significantly reduced. The lockdown forced many people to work from home and limit contact not only with friends but also with close family (parents, children, and siblings). Limiting contact with other people was a big challenge for most of our respondents, especially those who were living alone and for those who previously led an active social life. Depending on their earlier lifestyle profile, for some, the bigger problem was the limitation of contact with the family, for others with friends, and for still others with co-workers.

I think that because I can’t meet up with anyone and that I’m not in a relationship , I miss having sex , and I think it will become even more difficult because it will be increasingly hard to meet anyone . (5 . 3_ M_39_single) . The number In the brackets at the end of the quotes marks the respondent’s number (according to Table 1 ) and the stage of the interview (after the dash), further is information about gender (F/M), age of the respondent and family status. Linguistic errors in the quotes reflect the spoken language of the respondents.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0258133.t001

Changes over time . Over the course of the 6 months of the study, an evolution in the attitudes to the restriction of face-to-face contact could be seen: from full acceptance, to later questioning its rationale. Initially (March and April), almost all the respondents understood the reasons for the isolation and were compliant. At the beginning, people were afraid of the unknown COVID-19. They were concerned that the tragic situation from Italy, which was intensively covered in the media, could repeat itself in Poland (stage 1–2 of the study). However, with time, the isolation started to bother them more and more, and they started to look for solutions to bypass the isolation guidelines (stage 3–4), both real (simply meeting each other) and mental (treating isolation only as a guideline and not as an order, perceiving the family as being less threatening than acquaintances or strangers in a store). The turning point was the long May weekend that, due to two public holidays (1 st and 3 rd May), has for many years been used as an opportunity to go away with family or friends. Many people broke their voluntary isolation during that time encouraged by information about the coming loosening of restrictions.

During the summer (stage 5 of the survey), practically no one was fully compliant with the isolation recommendations anymore. At that time, a growing familiarity could be observed with COVID-19 and an increasing tendency to talk about it as “one of many diseases”, and to convince oneself that one is not at risk and that COVID-19 is no more threatening than other viruses. Only a small group of people consciously failed to comply with the restrictions of contact with others from the very beginning of the pandemic. This behavior was mostly observed among people who were generally less anxious and less afraid of COVID-19.

I’ve had enough. I’ve had it with sitting at home. Okay, there’s some kind of virus, it’s as though it’s out there somewhere; it’s like I know 2 people who were infected but they’re still alive, nothing bad has happened to anyone. It’s just a tiny portion of people who are dying. And is it really such a tragedy that we have to be locked up at home? Surely there’s an alternative agenda there? (17.4_F_35_Adult and child)

Ways of dealing . In the initial phase, when almost everyone accepted this restriction and submitted to it, the use of communication platforms for social meetings increased (see Ways of dealing with challenges in Table 1 ) . Meetings on communication platforms were seen as an equivalent of the previous face-to-face contact and were often even accompanied by eating or drinking alcohol together. However, over time (at around stage 4–5 of the study) people began to feel that such contact was an insufficient substitute for face-to-face meetings and interest in online meetings began to wane. During this time, however, an interesting phenomenon could be seen, namely, that for many people the family was seen as a safer environment than friends, and definitely safer than strangers. The belief was that family members would be honest about being sick, while strangers not necessarily, and—on an unconscious level—the feeling was that the “family is safe”, and the “family can’t hurt them”.

When it became clear that online communication is an insufficient substitute for face-to-face contacts, people started to meet up in real life. However, a change in many behaviors associated with meeting people is clearly visible, e.g.: refraining from shaking hands, refraining from cheek kissing to greet one another, and keeping a distance during a conversation.

I can’t really say that I could ‘feel’ Good Friday or Holy Saturday. On Sunday, we had breakfast together with my husband’s family and his sister. We were in three different places but we connected over Skype. Later, at noon, we had some coffee with my parents, also over Skype. It’s obvious though that this doesn’t replace face-to-face contact but it’s always some form of conversation. (9.3_F_25_Couple, no children)

Challenge 2 –restrictions on movement and travel . In contrast to the restrictions on contact with other people, the restrictions on movement and the closing of borders were perceived more negatively and posed bigger challenges for some people (especially those who used to do a lot of travelling). In this case, it was less clear why these regulations were introduced (especially travel restrictions within the country). Moreover, travel restrictions, particularly in the case of international travels, were associated with a limitation of civil liberties. The limitation (or complete ban) on travelling abroad in the Polish situation evoked additional connotations with the communist times, that is, with the fact that there was no freedom of movement for Polish citizens (associations with totalitarianism and dictatorship). Interestingly, the lack of acceptance of this restriction was also manifested by people who did not travel much. Thus, it was not just a question of restricting travelling abroad but more of restricting the potential opportunity (“even if I’m not planning on going anywhere, I know I still can”).

Limitations on travelling around the country were particularly negatively felt by families with children, where parents believe that regular exercise and outings are necessary for the proper development of their children. For parents, it was problematic to accept the prohibition of leaving the house and going to the playground (which remained closed until mid-May). Being outdoors was perceived as important for maintaining immunity (exercise as part of a healthy lifestyle), therefore, people could not understand the reason underlying this restriction and, as a consequence, often did not accept it.

I was really bothered by the very awareness that I can’t just jump in my car or get on a plane whenever I want and go wherever I want. It’s not something that I have to do on a daily basis but freedom of movement and travelling are very important for me. (14.2_M_55_Two adults and children)

Changes over time . The travel and movement limitations, although objectively less severe for most people, aroused much greater anger than the restrictions on social contact. This was probably due to a greater sense of misunderstanding as to why these rules were being introduced in the first place. Moreover, they were often communicated inconsistently and chaotically (e.g., a ban on entering forests was introduced while, at the same time, shopping malls remained open and masses were allowed to attend church services). This anger grew over time—from interview to interview, the respondents’ irritation and lack of acceptance of this was evident (culminating in the 3 rd -4 th stage of the study). The limitation of mobility was also often associated with negative consequences for both health and the economy. Many people are convinced that being in the open air (especially accompanied by physical activity) strengthens immunity, therefore, limiting such activity may have negative health consequences. Some respondents pointed out that restricting travelling, the use of hotels and restaurants, especially during the holiday season, will have serious consequences for the existence of the tourism industry.

I can’t say I completely agree with these limitations because it’s treating everything selectively. It’s like the shopping mall is closed, I can’t buy any shoes but I can go to a home improvement store and buy some wallpaper for myself. So I don’t see the difference between encountering people in a home improvement store and a shopping mall. (18.2_F_48_Two adults and children)

Ways of dealing . Since the restriction of movement and travel was more often associated with pleasure-related behaviors than with activities necessary for living, the compensations for these restrictions were usually also from the area of hedonistic behaviors. In the statements of our respondents, terms such as “indulging” or “rewarding oneself” appeared, and behaviors such as throwing small parties at home, buying better alcohol, sweets, and new clothes were observed. There were also increased shopping behaviors related to hobbies (sometimes hobbies that could not be pursued at the given time)–a kind of “post-pandemic” shopping spree (e.g., a new bike or new skis).

Again, the reaction to this restriction also depended on the level of fear of the COVID-19 disease. People who were more afraid of being infected accepted these restrictions more easily as it gave them the feeling that they were doing something constructive to protect themselves from the infection. Conversely, people with less fears and concerns were more likely to rebel and break these bans and guidelines.

Another way of dealing with this challenge was making plans for interesting travel destinations for the post-pandemic period. This was especially salient in respondents with an active lifestyle in the past and especially visible during the 5 th stage of the study.

Today was the first day when I went to the store (due to being in quarantine after returning from abroad). I spent loads of money but I normally would have never spent so much on myself. I bought sweets and confectionery for Easter time, some Easter chocolates, too. I thought I’d do some more baking so I also bought some ingredients to do this. (1.2_ F_25_single)

Challenge 3 –necessary change in active lifestyle . Many of the limitations related to COVID-19 were a challenge for people with an active lifestyle who would regularly go to the cinema, theater, and gym, use restaurants, and do a lot of travelling. For those people, the time of the COVID constraints has brought about huge changes in their lifestyle. Most of their activities were drastically restricted overnight and they suddenly became domesticated by force, especially when it was additionally accompanied by a transition to remote work.

Compulsory spending time at home also had serious consequences for people with school-aged children who had to confront themselves with the distance learning situation of their children. The second challenge for families with children was also finding (or helping find) activities for their children to do in their free time without leaving the house.

I would love to go to a restaurant somewhere. We order food from the restaurant at least once a week, but I’d love to go to the restaurant. Spending time there is a different way of functioning. It is enjoyable and that is what I miss. I would also go to the cinema, to the theater. (13.3_M_46_Two adults and child.)

Changes over time . The nuisance of restrictions connected to an active lifestyle depended on the level of restrictions in place at a given time and the extent to which a given activity could be replaced by an alternative. Moreover, the response to these restrictions depended more on the individual differences in lifestyle rather than on the stage of the interview (except for the very beginning, when the changes in lifestyle and everyday activities were very sudden).

I miss that these restaurants are not open . And it’s not even that I would like to eat something specific . It is in all of this that I miss such freedom the most . It bothers me that I have no freedom . And I am able to get used to it , I can cook at home , I can order from home . But I just wish I had a choice . (2 . 6_F_27_single ).

Ways of dealing . In the initial phase of the pandemic (March-April—stage 1–3 of the study), when most people were afraid of the coronavirus, the acceptance of the restrictions was high. At the same time, efforts were made to find activities that could replace existing ones. Going to the gym was replaced by online exercise, and going to the cinema or theater by intensive use of streaming platforms. In the subsequent stages of the study, however, the respondents’ fatigue with these “substitutes” was noticeable. It was then that more irritation and greater non-acceptance of certain restrictions began to appear. On the other hand, the changes or restrictions introduced during the later stages of the pandemic were less sudden than the initial ones, so they were often easier to get used to.

I bought a small bike and even before that we ordered some resistance bands to work out at home, which replace certain gym equipment and devices. […] I’m considering learning a language. From the other online things, my girlfriend is having yoga classes, for instance. (7.2_M_28_Couple, no children)

Challenge 4 –boredom , monotony . As has already been shown, for many people, the beginning of the pandemic was a huge change in lifestyle, an absence of activities, and a resulting slowdown. It was sometimes associated with a feeling of weariness, monotony, and even of boredom, especially for people who worked remotely, whose days began to be similar to each other and whose working time merged with free time, weekdays with the weekends, and free time could not be filled with previous activities.

In some way, boredom. I can’t concentrate on what I’m reading. I’m trying to motivate myself to do such things as learning a language because I have so much time on my hands, or to do exercises. I don’t have this balance that I’m actually doing something for myself, like reading, working out, but also that I’m meeting up with friends. This balance has gone, so I’ve started to get bored with many things. Yesterday I felt that I was bored and something should start happening. (…) After some time, this lack of events and meetings leads to such immense boredom. (1.5_F_25_single)

Changes over time . The feeling of monotony and boredom was especially visible in stage 1 and 2 of the study when the lockdown was most restrictive and people were knocked out of their daily routines. As the pandemic continued, boredom was often replaced by irritation in some, and by stagnation in others (visible in stages 3 and 4 of the study) while, at the same time, enthusiasm for taking up new activities was waning. As most people were realizing that the pandemic was not going to end any time soon, a gradual adaptation to the new lifestyle (slower and less active) and the special pandemic demands (especially seen in stage 5 and 6 of the study) could be observed.

But I see that people around me , in fact , both family and friends , are slowly beginning to prepare themselves for more frequent stays at home . So actually more remote work , maybe everything will not be closed and we will not be locked in four walls , but this tendency towards isolation or self-isolation , such a deliberate one , appears . I guess we are used to the fact that it has to be this way . (15 . 6_M_43_Two adults and child) .

Ways of dealing . The answer to the monotony of everyday life and to finding different ways of separating work from free time was to stick to certain rituals, such as “getting dressed for work”, even when work was only by a computer at home or, if possible, setting a fixed meal time when the whole family would gather together. For some, the time of the beginning of the pandemic was treated as an extra vacation. This was especially true of people who could not carry out their work during the time of the most severe restrictions (e.g., hairdressers and doctors). For them, provided that they believed that everything would return to normal and that they would soon go back to work, a “vacation mode” was activated wherein they would sleep longer, watch a lot of movies, read books, and generally do pleasant things for which they previously had no time and which they could now enjoy without feeling guilty. Another way of dealing with the monotony and transition to a slower lifestyle was taking up various activities for which there was no time before, such as baking bread at home and cooking fancy dishes.

I generally do have a set schedule. I begin work at eight. Well, and what’s changed is that I can get up last minute, switch the computer on and be practically making my breakfast and coffee during this time. I do some work and then print out some materials for my younger daughter. You know, I have work till four, I keep on going up to the computer and checking my emails. (19.1_F_39_Two adults and children)

Challenge 5 –uncertainty about the future . Despite the difficulties arising from the circumstances and limitations described above, it seems that psychologically, the greatest challenge during a pandemic is the uncertainty of what will happen next. There was a lot of contradictory information in the media that caused a sense of confusion and heightened the feeling of anxiety.

I’m less bothered about the changes that were put in place and more about this concern about what will happen in the future. Right now, it’s like there’s these mood swings. […] Based on what’s going on, this will somehow affect every one of us. And that’s what I’m afraid of. The fact that someone will not survive and I have no way of knowing who this could be—whether it will be me or anyone else, or my dad, if somehow the coronavirus will sneak its way into our home. I simply don’t know. I’m simply afraid of this. (10.1_F_55_Couple, no children)

Changes over time . In the first phase of the pandemic (interviews 1–3), most people felt a strong sense of not being in control of the situation and of their own lives. Not only did the consequences of the pandemic include a change in lifestyle but also, very often, the suspension of plans altogether. In addition, many people felt a strong fear of the future, about what would happen, and even a sense of threat to their own or their loved ones’ lives. Gradually (interview 4), alongside anxiety, anger began to emerge about not knowing what would happen next. At the beginning of the summer (stage 5 of the study), most people had a hope of the pandemic soon ending. It was a period of easing restrictions and of opening up the economy. Life was starting to look more and more like it did before the pandemic, fleetingly giving an illusion that the end of the pandemic was “in sight” and the vision of a return to normal life. Unfortunately, autumn showed that more waves of the pandemic were approaching. In the interviews of the 6 th stage of the study, we could see more and more confusion and uncertainty, a loss of hope, and often a manifestation of disagreement with the restrictions that were introduced.

This is making me sad and angry. More angry, in fact. […] I don’t know what I should do. Up until now, there was nothing like this. Up until now, I was pretty certain of what I was doing in all the decisions I was making. (14.4_M_55_Two adults and children)

Ways of dealing . People reacted differently to the described feeling of insecurity. In order to reduce the emerging fears, some people searched (sometimes even compulsively) for any information that could help them “take control” of the situation. These people searched various sources, for example, information on the number of infected persons and the number of deaths. This knowledge gave them the illusion of control and helped them to somewhat reduce the anxiety evoked by the pandemic. The behavior of this group was often accompanied by very strict adherence to all guidelines and restrictions (e.g., frequent hand sanitization, wearing a face mask, and avoiding contact with others). This behavior increased the sense of control over the situation in these people.

A completely opposite strategy to reducing the feeling of uncertainty which we also observed in some respondents was cutting off information in the media about the scale of the disease and the resulting restrictions. These people, unable to keep up with the changing information and often inconsistent messages, in order to maintain cognitive coherence tried to cut off the media as much as possible, assuming that even if something really significant had happened, they would still find out.

I want to keep up to date with the current affairs. Even if it is an hour a day. How is the pandemic situation developing—is it increasing or decreasing. There’s a bit of propaganda there because I know that when they’re saying that they have the situation under control, they can’t control it anyway. Anyhow, it still has a somewhat calming effect that it’s dying down over here and that things aren’t that bad. And, apart from this, I listen to the news concerning restrictions, what we can and can’t do. (3.1_F_54_single)

Discussion and conclusions

The results of our study showed that the five greatest challenges resulting from the COVID-19 pandemic are: limitations of direct contact with people, restrictions on movement and travel, change in active lifestyle, boredom and monotony, and finally uncertainty about the future. As we can see the spectrum of problems resulting from the pandemic is very wide and some of them have an impact on everyday functioning and lifestyle, some other influence psychological functioning and well-being. Moreover, different people deal with these problems differently and different changes in everyday life are challenging for them. The first challenge of the pandemic COVID-19 problem is the consequence of the limitation of direct contact with others. This regulation has very strong psychological consequences in the sense of loneliness and lack of closeness. Initially, people tried to deal with this limitation through the use of internet communicators. It turned out, however, that this form of contact for the majority of people was definitely insufficient and feelings of deprivation quickly increased. As much data from psychological literature shows, contact with others can have great psychological healing properties [e.g., 29 ]. The need for closeness is a natural need in times of crisis and catastrophes [ 30 ]. Unfortunately, during the COVID-19 pandemic, the ability to meet this need was severely limited by regulations. This led to many people having serious problems with maintaining a good psychological condition.

Another troubling limitation found in our study were the restrictions on movement and travel, and the associated restrictions of most activities, which caused a huge change in lifestyle for many people. As shown in previous studies, travel and diverse leisure activities are important predictors of greater well-being [ 36 ]. Moreover, COVID-19 pandemic movement restrictions may be perceived by some people as a threat to human rights [ 37 ], which can contribute to people’s reluctance to accept lockdown rules.

The problem with accepting these restrictions was also related to the lack of understanding of the reasons behind them. Just as the limitation in contact with other people seemed understandable, the limitations related to physical activity and mobility were less so. Because of these limitations many people lost a sense of understanding of the rules and restrictions being imposed. Inconsistent communication in the media—called by some researchers the ‘infodemic’ [ 18 ], as well as discordant recommendations in different countries, causing an increasing sense of confusion in people.

Another huge challenge posed by the current pandemic is the feeling of uncertainty about the future. This feeling is caused by constant changes in the rules concerning daily functioning during the pandemic and what is prohibited and what is allowed. People lose their sense of being in control of the situation. From the psychological point of view, a long-lasting experience of lack of control can cause so-called learned helplessness, a permanent feeling of having no influence over the situation and no possibility of changing it [ 38 ], which can even result in depression and lower mental and physical wellbeing [ 39 ]. Control over live and the feeling that people have an influence on what happens in their lives is one of the basic rules of crisis situation resilience [ 30 ]. Unfortunately, also in this area, people have huge deficits caused by the pandemic. The obtained results are coherent with previous studies regarding the strategies harnessed to cope with the pandemic [e.g., 5 , 10 , 28 , 33 ]. For example, some studies showed that seeking social support is one of the most common strategies used to deal with the coronavirus pandemic [ 33 , 40 ]. Other ways to deal with this situation include distraction, active coping, and a positive appraisal of the situation [ 41 ]. Furthermore, research has shown that simple coping behaviors such as a healthy diet, not reading too much COVID-19 news, following a daily routine, and spending time outdoors may be protective factors against anxiety and depressive symptoms in times of the coronavirus pandemic [ 41 ].

This study showed that the acceptance of various limitations, and especially the feeling of discomfort associated with them, depended on the person’s earlier lifestyle. The more active and socializing a person was, the more restrictions were burdensome for him/her. The second factor, more of a psychological nature, was the fear of developing COVID-19. In this case, people who were more afraid of getting sick were more likely to submit to the imposed restrictions that, paradoxically, did not reduce their anxiety, and sometimes even heightened it.

Limitations of the study.

While the study shows interesting results, it also has some limitations. The purpose of the study was primarily to capture the first response to problems resulting from a pandemic, and as such its design is not ideal. First, the study participants are not diverse as much as would be desirable. They are mostly college-educated and relatively well off, which may influence how they perceive the pandemic situation. Furthermore, the recruitment was done by searching among the further acquaintances of the people involved in the study, so there is a risk that all the people interviewed come from a similar background. It would be necessary to conduct a study that also describes the reaction of people who are already in a more difficult life situation before the pandemic starts.

Moreover, it would also be worthwhile to pay attention to the interviewers themselves. All of the moderators were female, and although gender effects on the quality of the interviews and differences between the establishment of relationships between women and men were not observed during the debriefing process, the topic of gender effects on the results of qualitative research is frequently addressed in the literature [ 42 , 43 ]. Although the researchers approached the process with reflexivity and self-criticism at all stages, it would have seemed important to involve male moderators in the study to capture any differences in relationship dynamics.

Practical implications.

The study presented has many practical implications. Decision-makers in the state can analyze the COVID-19 pandemic crisis in a way that avoids a critical situation involving other infectious diseases in the future. The results of our study showing the most disruptive effects of the pandemic on people can serve as a basis for developing strategies to deal with the effects of the crisis so that it does not translate into a deterioration of the public’s mental health in the future.

The results of our study can also provide guidance on how to communicate information about restrictions in the future so that they are accepted and respected (for example by giving rational explanations of the reasons for introducing particular restrictions). In addition, the results of our study can also be a source of guidance on how to deal with the limitations that may arise in a recurrent COVID-19 pandemic, as well as other emergencies that could come.

The analysis of the results showed that the COVID-19 pandemic, and especially the lockdown periods, are a particular challenge for many people due to reduced social contact. On the other hand, it is social contacts that are at the same time a way of a smoother transition of crises. This knowledge should prompt decision-makers to devise ways to ensure pandemic safety without drastically limiting social contacts and to create solutions that give people a sense of control (instead of depriving it of). Providing such solutions can reduce the psychological problems associated with a pandemic and help people to cope better with it.

Conclusions

As more and more is said about the fact that the COVID-19 pandemic may not end soon and that we are likely to face more waves of this disease and related lockdowns, it is very important to understand how the different restrictions are perceived, what difficulties they cause and what are the biggest challenges resulting from them. For example, an important element of accepting the restrictions is understanding their sources, i.e., what they result from, what they are supposed to prevent, and what consequences they have for the fight against the pandemic. Moreover, we observed that the more incomprehensible the order was, the more it provoked to break it. This means that not only medical treatment is extremely important in an effective fight against a pandemic, but also appropriate communication.

The results of our study showed also that certain restrictions cause emotional deficits (e.g., loneliness, loss of sense of control) and, consequently, may cause serious problems with psychological functioning. From this perspective, it seems extremely important to understand which restrictions are causing emotional problems and how they can be dealt with in order to reduce the psychological discomfort associated with them.

Supporting information

S1 table. a full description of the changes occurring in poland at the time of the study..

https://doi.org/10.1371/journal.pone.0258133.s001

S2 Table. Characteristics of study participants.

https://doi.org/10.1371/journal.pone.0258133.s002

S1 Dataset. Transcriptions from the interviews.

https://doi.org/10.1371/journal.pone.0258133.s003

• 1. JHU CSEE. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. 2020 [cited 2021 Jun 1]. https://github.com/CSSEGISandData/COVID-19#covid-19-data-repository-by-the-center-for-systems-science-and-engineering-csse-at-johns-hopkins-university .
• View Article
• PubMed/NCBI
• Google Scholar
• 5. Gerhold L. COVID-19: Risk perception and coping strategies. Results from a survey in Germany. PsyArXiv [Preprint]. 2020 Mar 25.
• 6. Nowakowska K. Rok z koronawirusem: Od paniki, przez luz, do fatalizmu. [A year with coronavirus—from panic through chill to fatalism]. Dziennik Gazeta Prawna [Internet]. 2021 Mar 4 [cited 2021 Jun 1]. https://www.gazetaprawna.pl/wiadomosci/kraj/artykuly/8111858,szczepienia-maseczki-rok-z-koronawirusem-zycie-codzienne.html
• 11. Fernandes N. Economic effects of coronavirus outbreak (COVID-19) on the world economy. IESE Business School Working Paper No. WP-1240-E. 2020 Mar 23.
• 15. ARC. Praca z domu w polskim wydaniu—badanie na zlecenie Gumtree.pl we współpracy z Randstat Polska [The Polish way of working from home—a study commissioned by Gumtree.pl in collaboration with Randstat Polska]. 2020 [cited 2021 Jun 1]. https://www.randstad.pl/strefa-pracownika/centrum-prasowe/badanie-gumtreepl-we-wspolpracy-z-randstad-potrafimy-sie-zorganizowac-choc-czasem-lubimy-sobie-poprzeszkadzac-praca-z-domu-w-polskim-wydaniu/
• 16. Sierpowska, I. O edukacji w czasie pandemii [On Education during pandemic]. Centrum Prasowe SWPS [Internet]. 2020 Sep 8. [cited 2021 Jun 1]. https://www.swps.pl/centrum-prasowe/informacje-prasowe/22390-o-edukacji-w-czasie-pandemii-2?dt=1622540060078
• 17. Polish Academy of Sciences. Understanding COVID-19. Report by the COVID-19 team at the President of the Polish Academy of Sciences. 2020 Sep 14. [Cited 2021 Jun 1]. https://informacje.pan.pl/images/2020/opracowanie-covid19-14-09-2020/ZrozumiecCovid19_opracowanie_PAN.pdf
• 25. Brown, K. The pandemic is not a natural disaster. The New Yorker [Internet]. 2020 Apr 13 [cited 2021 Jun 1]. https://www.newyorker.com/culture/annals-of-inquiry/the-pandemic-is-not-a-natural-disaster .
• 34. Maison D. Qualitative marketing research. Understanding consumer behaviour. London: Routledge; 2019.
• 36. Argyle M. Causes and correlates of happiness. In: Kahneman D, Diener E, Schwarz N, editors. Well-Being: The Foundations of Hedonic Psychology. New York: Russell Sage Foundation; 1999. p. 353–373.

• Business Management

400 Trending Business Management Research Topics in 2024

Home Blog Business Management 400 Trending Business Management Research Topics in 2024

Business management is crucial for competitiveness and profitability in today's fast-paced world. It involves understanding business structure, finance, marketing, and strategy. Pursuing a postgraduate course, like PGDM, often requires a well-researched paper to launch one's career. The main challenge is selecting a relevant, trending research topic. To assist, here are ten current business management research topics for 2024, focusing on technological advancements and innovative leadership strategies. Enrolling in Business Management training courses can further enhance your skills and knowledge, propelling your career to new heights. Let's explore these cutting-edge topics together for career growth.

Business Management Research Topics [Based on Different Industries]

A. business management research topics for business administration.

• Data analytics’ role in company performance and decision-making.
• Revolution of firm operations and strategy due to artificial intelligence.
• How sustainable business practices affect a company’s financial performance.
• Blockchain technology’s role in business.
• Impact of fintech on traditional financial institutions.
• How digital transformation affects organizational culture.
• Consequences of social media marketing for customer engagement.
• Impact of the gig economy on the traditional employment model.
• Abuse experienced by women in the workplace.
• Effects of the COVID-19 pandemic on global supply chain management.
• Impact of agile methodologies on business management.
• The role of emotional intelligence in business leadership.
• Outsourcing and its effects on business efficiency.
• Implementing corporate governance for better decision-making.
• The influence of consumer behavior on marketing strategies.
• E-commerce trends and their impact on retail businesses.
• Strategies for managing business risks and uncertainties.
• Ethical considerations in business strategy formulation.
• Role of business analytics in strategic planning.
• Organizational resilience in times of economic downturns.
• Corporate philanthropy and its impact on business reputation.
• Change management strategies for business growth.
• Impact of employee engagement on organizational performance.
• Role of innovation hubs in business development.
• The influence of global trade policies on local businesses.
• Business model innovation in the digital age.
• Customer relationship management (CRM) systems and their impact.
• The role of leadership development programs in businesses.
• Strategic alliances and partnerships in business growth.
• The impact of business process reengineering on performance.
• The effectiveness of telecommuting in business operations.
• Business continuity planning in disaster management.
• The impact of organizational structure on business efficiency.
• The role of corporate governance in fraud prevention.
• The influence of market segmentation on business strategies.
• The role of strategic management in business growth.
• The impact of regulatory changes on business operations.
• The role of knowledge management in business success.
• The impact of employee training and development on performance.
• Strategies for improving business process efficiency.
• The role of innovation in competitive advantage.
• The impact of globalization on small businesses.
• The role of social responsibility in business ethics.
• Strategies for enhancing customer loyalty.
• The impact of digital marketing on business growth.
• The role of strategic planning in organizational success.
• The influence of leadership styles on business outcomes.
• Strategies for managing business transformation.
• The impact of technological advancements on business operations.
• The role of corporate social responsibility in branding.
• The effectiveness of business incubators in start-up success.
• The role of organizational culture in business performance.
• The impact of financial management on business sustainability.
• The influence of business intelligence on decision-making.
• Strategies for improving customer satisfaction.

B. Business Management Research Topics for Accounting and Finance

• Asset pricing and financial markets
• Business history
• Corporate finance
• Corporate governance
• Credit management
• Financial accounting and auditing
• Organizations: ownership, governance and performance
• SME finance
• Sustainable finance and ESG
• Venture capital and private equity
• Banking and financial intermediation
• Behavioral finance
• The effect of digital currencies on global finance.
• Forensic accounting and fraud detection.
• Impact of financial regulations on banking operations.
• Corporate financial planning and risk management.
• Trends in international financial reporting standards.
• The role of auditing in corporate governance.
• Financial forecasting techniques in business planning.
• The impact of economic crises on financial markets.
• Mergers and acquisitions: Financial implications and outcomes.
• The role of financial technology in modern banking.
• Sustainable investment strategies and their impact.
• Corporate social responsibility and financial performance.
• Financial literacy and its importance for small businesses.
• The role of credit rating agencies in financial markets.
• Comparative analysis of different investment portfolios.
• The impact of taxation policies on business growth.
• Financial management practices in non-profit organizations.
• Trends in global investment and capital flows.
• The role of financial markets in economic development.
• Ethical issues in financial reporting.
• Financial risk management in multinational corporations.
• The role of financial intermediaries in economic growth.
• The impact of financial innovations on market stability.
• Financial distress and corporate restructuring.
• The role of hedge funds in financial markets.
• The influence of monetary policy on financial markets.
• Behavioral finance and investor psychology.
• The impact of interest rates on investment decisions.
• Corporate governance and shareholder value.
• The role of venture capital in entrepreneurial success.
• Financial market efficiency and anomalies.
• The influence of financial globalization on local markets.
• Financial inclusion and its impact on economic development.
• The role of institutional investors in corporate governance.
• The impact of fiscal policy on financial markets.
• Financial market integration and economic growth.
• The role of financial regulation in preventing crises.
• The influence of economic indicators on financial markets.
• Financial planning for retirement and its importance.
• The role of microfinance in poverty alleviation.
• Financial implications of environmental sustainability.
• The impact of demographic changes on financial markets.
• The role of corporate finance in strategic decision-making.
• Financial analysis and valuation of companies.
• The influence of globalization on financial reporting standards.

C. Business Management Research Topics for Economics

• Environment, infrastructure, innovation and the circular economy
• Work, labour and organisation
• Financialisation and globalisation
• Development and wellbeing
• The macro economy and macroeconomic policy
• The impact of trade wars on global economies.
• Economic policies and their effect on unemployment rates.
• Economic implications of climate change policies.
• The future of globalization in the post-pandemic world.
• Behavioral economics and consumer decision-making.
• Economic growth and income inequality.
• The role of government subsidies in economic development.
• Economic effects of demographic changes.
• Digital economy and its impact on traditional industries.
• The relationship between inflation and interest rates.
• The impact of economic sanctions on global trade.
• The economics of renewable energy sources.
• Economic policies for sustainable development.
• The role of entrepreneurship in economic growth.
• Economic impacts of technological advancements.
• Comparative analysis of economic systems.
• The effect of financial crises on emerging markets.
• Economic policies for managing public debt.
• The impact of immigration on labor markets.
• The role of international trade in economic development.
• The impact of monetary policy on economic stability.
• The influence of fiscal policy on economic growth.
• Economic implications of global health crises.
• The role of education in economic development.
• The impact of urbanization on economic growth.
• Economic policies for reducing income inequality.
• The influence of political stability on economic development.
• The role of innovation in economic growth.
• Economic effects of environmental regulations.
• The impact of global economic integration on local economies.
• Economic policies for promoting entrepreneurship.
• The influence of cultural factors on economic behavior.
• The impact of technological advancements on labor markets.
• Economic implications of international trade agreements.
• The role of government intervention in market economies.
• The impact of population growth on economic development.
• Economic policies for managing inflation.
• The influence of global economic trends on local economies.
• The impact of economic crises on poverty levels.
• The role of social welfare programs in economic development.
• Economic implications of digital currencies.
• The influence of economic policies on business cycles.
• The impact of economic inequality on social stability.

D. Business Management Research  Topics for International Business

• International business policy, SDGs and “grand challenges”
• International business, migration and society
• Global health and international business
• Cross-cultural management, diversity and inclusion
• The theory of the multinational enterprise (MNE)
• The governance of global value chains (GVCs)
• Emerging market multinational enterprises (EMNEs)
• The impact of political instability on international business.
• Strategies for managing cultural differences in global teams.
• The role of international trade agreements in business expansion.
• Global business strategies in emerging markets.
• International marketing challenges and opportunities.
• The impact of Brexit on European businesses.
• Global supply chain management best practices.
• The role of global business networks in innovation.
• Cross-border mergers and acquisitions: Challenges and strategies.
• The influence of global economic trends on business strategy.
• International business ethics and legal considerations.
• The impact of digital globalization on traditional business models.
• Strategies for entering new international markets.
• The role of global leadership in multinational corporations.
• International business communication challenges.
• The impact of global crises on international business operations.
• Managing global talent and human resources.
• The role of expatriates in international business.
• Global financial management practices.
• The impact of cultural intelligence on international business success.
• Strategies for managing international business risks.
• The role of international joint ventures in business growth.
• The influence of global consumer behavior on marketing strategies.
• The impact of international regulatory changes on business operations.
• Strategies for managing cross-cultural negotiations.
• The role of global logistics in supply chain management.
• The influence of international economic policies on business strategy.
• The impact of global technological advancements on business operations.
• Strategies for managing international business partnerships.
• The role of international business in economic development.
• The impact of global trade policies on business competitiveness.
• The influence of international market trends on business strategy.
• Strategies for managing international business expansion.
• The role of global innovation hubs in business development.
• The impact of cultural differences on international business negotiations.
• The influence of global financial markets on business operations.
• Strategies for managing international business compliance.
• The role of international business in promoting sustainability.
• The impact of global economic integration on business strategy.
• The influence of cultural diversity on international business success.
• Strategies for managing international business innovation.
• The role of global entrepreneurship in business growth.
• The impact of international trade disputes on business operations.
• The influence of global economic shifts on business strategy.
• Strategies for managing international business risks and uncertainties.

E. Business Management Project Topics for Management 

• Organizational strategy
• Global supply chains
• Leadership and performance
• Technology and innovation
• Digital transformation
• Sustainability
• Information management and information systems
• Learning and change
• Human information processing
• Decision making
• Strategies for managing remote teams.
• The role of leadership in fostering innovation.
• Performance management systems in modern businesses.
• Conflict management and resolution strategies.
• The impact of organizational culture on employee performance.
• Strategic human resource management practices.
• The role of technology in transforming management practices.
• Change management in dynamic business environments.
• Effective communication strategies in management.
• The impact of leadership styles on organizational change.
• Crisis management and business continuity planning.
• Employee motivation techniques in diverse workforces.
• The role of mentoring in leadership development.
• Strategic planning in uncertain business environments.
• The influence of corporate culture on business success.
• Managing innovation in established companies.
• The impact of globalization on management practices.
• Decision-making processes in business management.
• Strategies for enhancing employee productivity.
• The role of ethics in business management.
• Managing diversity and inclusion in the workplace.
• The impact of emotional intelligence on leadership effectiveness.
• Strategies for managing organizational change.
• The role of corporate governance in management practices.
• Managing cross-functional teams for business success.
• The influence of digital transformation on management practices.
• Strategies for improving employee engagement and retention.
• The role of strategic alliances in business growth.
• Managing work-life balance in modern organizations.
• The impact of leadership development programs on business performance.
• Strategies for fostering a culture of continuous improvement.
• The role of management consulting in business success.
• Managing organizational conflicts and their resolution.
• The influence of corporate social responsibility on management practices.
• Strategies for managing business process reengineering.
• The role of technology in enhancing management practices.
• Managing employee performance through effective feedback.
• The impact of leadership styles on team dynamics.
• Strategies for improving organizational communication.
• The role of strategic management in business success.
• Managing organizational growth and scalability.
• The influence of corporate ethics on management decisions.
• Strategies for managing business transformation and change.
• The role of human resource management in organizational development.
• Managing innovation and creativity in the workplace.

F. Project Topics for Marketing

• Corporate responsibility and sustainability
• Green marketing and advertising
• International marketing
• Cross cultural buyer-seller relationships
• Consumer buying behaviour
• Analysis of consumer heterogeneous preferences and discrete choice analysis
• Retailing and store choice analysis
• Branding and brand equity
• Formulating and implementing sustainability marketing strategies: Bridging the gap
• Marketing strategy making
• Emergent marketing strategy and decision making in marketing organizations
• Export marketing strategy and performance
• Sustainable strategies of multinational corporations
• Standardizations/adaptation of international service offerings
• International marketing process standardization/adaptation
• Strategies for leveraging user-generated content in marketing campaigns.
• The impact of augmented reality on consumer purchasing decisions.
• Marketing strategies for virtual reality products.
• The influence of personalization on consumer loyalty.
• The effectiveness of loyalty programs in retaining customers.
• The role of neuromarketing in understanding consumer behavior.
• Strategies for marketing to Generation Z.
• The impact of voice search on digital marketing strategies.
• The influence of podcast advertising on brand awareness.
• Marketing strategies for crowdfunding campaigns.
• The role of gamification in enhancing customer engagement.
• The impact of blockchain technology on marketing practices.
• The effectiveness of omnichannel marketing strategies.
• The influence of artificial intelligence on customer service.
• Marketing strategies for non-profit organizations.
• The impact of eco-labeling on consumer purchasing behavior.
• The role of data privacy regulations on digital marketing.
• The influence of interactive content on consumer engagement.
• Marketing strategies for subscription box services.
• The impact of influencer partnerships on brand reputation.
• The effectiveness of cross-promotion in increasing sales.
• The role of predictive analytics in marketing strategy.
• The influence of social commerce on consumer behavior.
• Marketing strategies for pop-up shops and temporary retail spaces.
• The impact of mobile payment systems on consumer spending.
• The role of virtual influencers in marketing campaigns.
• The effectiveness of geotargeting in local marketing.
• The influence of ethical branding on consumer trust.
• Marketing strategies for cause-related marketing campaigns.
• The impact of social media challenges on brand engagement.
• The role of experiential marketing in building brand loyalty.
• The influence of mobile gaming on advertising effectiveness.
• The effectiveness of remarketing campaigns in conversion rates.
• The impact of chatbots on customer experience in e-commerce.
• The role of video marketing in enhancing brand storytelling.
• Marketing strategies for health and wellness products.
• The influence of social proof on consumer purchasing decisions.
• The effectiveness of SMS marketing in reaching target audiences.
• The impact of subscription models on customer retention.
• The role of interactive advertising in consumer engagement.
• Marketing strategies for sustainable fashion brands.
• The influence of visual content on social media engagement.
• The effectiveness of email segmentation in increasing open rates.
• The impact of digital wallets on consumer behavior.
• The role of affiliate marketing in driving sales.

G. Business Management Research Topics for Employment Relations

• Labour mobility, migration and citizenship
• Markets, flexibilization and social protection
• Voice, representation and social movement
• Digitalization, automation, platformisation, and the future of work
• Between professions and precarity: the new world of work
• Changing structures of governance and organisation
• Employment, skills and occupations
• The impact of flexible working arrangements on employee productivity.
• Strategies for managing employee relations in remote work environments.
• The role of employee resource groups in promoting diversity and inclusion.
• The impact of gig economy trends on traditional employment relations.
• Strategies for handling workplace harassment and discrimination.
• The role of mental health initiatives in employee well-being.
• The impact of automation on employment relations in manufacturing.
• Strategies for managing employee grievances and disputes.
• The role of labor unions in the modern workforce.
• The impact of cultural diversity on employee relations.
• Strategies for fostering a positive organizational culture.
• The role of employee feedback in improving workplace policies.
• The impact of generational differences on employee relations.
• Strategies for enhancing employee participation in decision-making.
• The role of work-life balance in employee satisfaction.
• The impact of telecommuting on team dynamics.
• Strategies for managing employee turnover in high-stress industries.
• The role of employee recognition programs in motivation.
• The impact of workplace wellness programs on employee productivity.
• Strategies for improving communication between management and employees.
• The role of training and development in employee engagement.
• The impact of job security on employee morale.
• Strategies for managing conflict in multicultural teams.
• The role of leadership styles in shaping employee relations.
• The impact of economic downturns on employment practices.
• Strategies for addressing employee burnout and fatigue.
• The role of corporate social responsibility in employee relations.
• The impact of remote work on employee collaboration.
• Strategies for enhancing employee loyalty and retention.
• The role of digital tools in managing employee relations.
• The impact of legal regulations on employment practices.
• Strategies for fostering innovation through employee engagement.
• The role of mentorship programs in career development.
• The impact of employee empowerment on organizational success.
• Strategies for managing employee relations in mergers and acquisitions.
• The role of conflict resolution training in improving workplace harmony.
• The impact of social media policies on employee behavior.
• Strategies for promoting ethical behavior in the workplace.
• The role of transparency in building employee trust.
• The impact of employee surveys on organizational improvement.
• Strategies for managing generational conflicts in the workplace.
• The role of flexible benefits in employee satisfaction.
• The impact of workplace design on employee productivity.
• Strategies for addressing the skills gap in the workforce.
• The role of employee advocacy in shaping company policies.

H. Project Topics for Business ethics topics

• Maintaining Compliance with Independent Contractors
• The perception of tax evasion ethics
• Consumer Rights to Privacy and Confidentiality
• The role of ethical leadership in fostering corporate integrity.
• Strategies for promoting transparency in business operations.
• The impact of corporate governance on ethical business practices.
• The role of ethics training programs in shaping employee behavior.
• The influence of corporate culture on ethical decision-making.
• Strategies for managing ethical dilemmas in the workplace.
• The impact of corporate social responsibility on business reputation.
• The role of whistleblowing policies in promoting ethical conduct.
• The influence of stakeholder engagement on ethical business practices.
• Strategies for ensuring compliance with ethical standards.
• The impact of ethical branding on consumer trust.
• The role of corporate ethics committees in governance.
• The influence of regulatory frameworks on business ethics.
• Strategies for fostering an ethical organizational culture.
• The impact of ethical supply chain management on brand reputation.
• The role of sustainability initiatives in ethical business practices.
• The influence of ethical marketing on consumer behavior.
• Strategies for addressing ethical issues in digital marketing.
• The impact of business ethics on corporate financial performance.
• The role of ethical considerations in mergers and acquisitions.
• The influence of corporate ethics on employee loyalty.
• Strategies for managing conflicts of interest in business.
• The impact of ethical leadership on organizational success.
• The role of ethics in strategic business planning.
• The influence of ethical practices on investor relations.
• Strategies for ensuring ethical compliance in global operations.
• The impact of ethics on corporate governance frameworks.
• The role of ethical innovation in business sustainability.
• The influence of corporate social responsibility on stakeholder trust.
• Strategies for managing ethical risks in business.
• The impact of ethical leadership on employee engagement.
• The role of ethics in business continuity planning.
• The influence of ethical considerations on product development.
• Strategies for promoting ethical behavior in customer service.
• The impact of corporate ethics on competitive advantage.
• The role of ethics in managing corporate social media presence.
• The influence of ethical practices on supply chain resilience.
• Strategies for fostering ethical behavior in remote teams.
• The impact of business ethics on brand equity.
• The role of ethical considerations in crisis management.
• The influence of corporate governance on ethical leadership.
• Strategies for integrating ethics into business strategy.
• The impact of ethical consumerism on marketing strategies.
• The role of ethical decision-making in corporate success.
• The influence of corporate ethics on organizational change.

What are Some Good Business Management Research Topics in 2024?

• Conflict Management in a Work Team
• The Role of Women in Business Management
• Issues that Affect the Management of Business Startups
• Consequences of Excessive Work in Business
• Why You Should Start a New Business After One Fails
• Importance of Inter-organizational Leadership and Networks
• How to Manage Organizational Crisis in Business
• Product and Service Development in a Strategic Alliance
• Innovation and Network Markets as a Business Strategy
• Social Enterprise and Entrepreneurship

Every aspect of business, like strategy, finance, operations, and management, is essential. So, it’s hard to say that a particular area of research is more significant. Choosing the best research topic in business management within your area of interest or specialization is one way to decide what your business management research project will be about. It is also a learning process and an opportunity to showcase your in-depth knowledge. 

But if you want to explore other options, write about trending issues and events in the business world, and learn something new, here’s a list of 10 research proposal topics in business management that can help you create an engaging and practical project. You can also take a CCBA training certification to learn more in-depth about business management. 

1. Conflict Management in a Work Team

With businesses going global, team management has escalated from merely managing people to guiding, mentoring and resolving conflicts among individuals. Teams with multicultural members from different departments are fertile ground for conflicts. If you are looking for international business management research topics, conflict management in work teams is an excellent option. 

This research will give you an insight into the various causes of conflict and different techniques and methods of conflict resolution within global multi-lingual and multi-cultural teams enabling you to lead teams successfully and keep disruptions minimal. Better teams translate to better productivity and, eventually, revenue. On the personal front, it means career growth, leadership roles, and higher pay scales for you.

2. The Role of Women in Business Management

In contemporary society, women have made notable strides in shattering patriarchal norms and embracing diverse opportunities and career paths, thereby demonstrating their strength and autonomy. While women encounter challenges in assuming leadership roles, often stemming from prevailing cultural attitudes, their presence in business management positions is more prevalent than commonly perceived. This prompts inquiry into the factors that contribute to the exceptional success of certain women in managerial positions and the unique value they bring to such roles. Exploring this subject through qualitative research could yield insightful findings regarding women's impact on business management.

3. Issues that Affect the Management of Business Startups

The COVID-19 pandemic drove everyone online and created a new digital startup ecosystem. However, while it may be easy to set up a digital business , sustenance, scaling, and growth are some of the challenges that follow. If you are entrepreneurial, your research title about business management should read something like “Challenges in the startup ecosystem.” Such research covers issues that affect the management of business startups. It covers the various factors that lead to success and the pitfalls and obstacles on the growth trajectory. It covers effective strategies to mitigate or work around challenges, and this is where you can get creative. Limiting your research to startups is okay, but you can also cover significant ground across other business models.

4. Consequences of Excessive Work in Business

Work-life balance is the buzzword in today’s business environment. If you choose to write your thesis on the impact of excessive work in business, it could well escalate to international levels as everyone talks about employee well-being, from corporates to SMEs and top management to HR. 

The single most significant reason behind this is the instances of early burnout seen in the past. Secondly, globalization is another cause for concern since people are often required to work multiple shifts. Lastly, the recent trend of post-Covid layoffs that have driven the need for side hustle makes it even more necessary to keep track of how hectic business operations are. 

5. Why You Should Start a New Business After One Fails

Failure is the steppingstone to success. Or so the saying goes. The recent outcrop of start-ups has proven this to be true. If one venture fails, do not give up. Learn from the experience and start again. Not only is that the mantra of the current generation, but it is also among the trending quantitative research topics in business management. 

The main objective and outcome of this business management research topic are to explore lessons learned from failures, the advantages of starting afresh, and the strategies for overcoming the fear of failure.

6. Importance of Inter-organizational Leadership and Networks

This research focuses on managing global networks in leadership roles. It is among the hot favorite research topics for business management students considering how businesses are going global. If you are an aspiring global entrepreneur or leader, you would want to know more about local and global inter-organizational networks, how things work, how people communicate, etc. Researching inter-organizational leadership and networks can provide insights into businesses' challenges and opportunities when building and maintaining relationships. Managing these relationships is another challenging part of the process, and that is what you will learn through this research. 

7. How to Manage Organizational Crisis in Business

Not only is crisis management a critical leadership skill, but today's turbulent business environment is fertile ground for an organizational crisis. Globalization, digitization, and the startup ecosystem have disrupted the environment. Barring corporates, a crisis can strike any business at any time and bailing out of that crisis is the responsibility of the business leadership. Managing an organizational crisis in business is a popular business management research paper topic, especially among MBA students, PGDM, and aspiring entrepreneurs.

8. Product and Service Development in a Strategic Alliance

When it comes to research paper topics related to business management, one area worth exploring is product bundling in a strategic alliance. The ICICI credit card offered to online customers of Amazon India is a classic example.

Development of such strategic products or services requires in-depth product knowledge, knowledge of finance, and of course, a strategic mindset. If you have a strategic mindset and interest in product management, this is one of your best business management research project topics.

9. Innovation and Network Markets as a Business Strategy

Innovation and Network marketing is an emerging and strategic business model for startups. When entrepreneurs need more resources to raise seed or venture capital for their businesses, they elect to market their products through networking. Social Media platforms like Facebook offer substantial networking opportunities. Choose this probe as your quantitative research topic for business management if you have entrepreneurial aspirations to understand every aspect of this business model and strategy in depth.

10. Social Enterprise and Entrepreneurship

Social enterprise is any business having a social objective and undertaking activities in the public interest. Writing a research paper on social enterprises and entrepreneurship will lead you to explore opportunities that can bring an innovative change in society and hold business potential. One thing to remember if you want to explore social enterprise and entrepreneurship as one of several business management research titles is that the organizational goal is primarily social impact rather than revenue generation. This research will make you more open to an inclusive idea of growth by bringing you closer to social causes, marginalized communities, and people thriving in them.

How to Find Business Management Research Topics?

This is just our list of hot and trending business research topics. To help you discover more research project topics on business management, here are some quick-follow tips:

1. Identify Your Interests

Start by making a list of the various aspects of business management that interest you. Rate them on a scale of 1-10, with one being the least liked and 10 being your most favorite. You can also narrow down your topic to a specific niche while seeking sample research topics in business management.

2. Read Academic Journals

You might want to conduct preliminary research on a few of the topics you shortlisted to see if something interesting jumps out at you. One way to do this is by reading academic journals related to your selected area of business management. Findings by earlier researchers may trigger innovative thought.

3. Attend Events

Attending business events like seminars, conferences, and webinars on topics of interest can help you narrow down your list of research topics related to business management. It is also an excellent way to gather knowledge about your area of interest as well as to grow your network.

4. Consult your supervisor or Mentor

Your thesis supervisor is a valuable resource when searching for the best research topics in business management. They can guide you about relevant research areas and help you identify potential research questions apart from guiding you on research presentation.

5. Use Online Resources

Many research journals online allow students access to research papers either free of cost or in exchange for a small fee. Explore this resource and sign up for a few that are relevant to your area of interest.

Business Management Research: Types and Methodologies

Business research, like any other research, involves the collection of data and information about your chosen topic, analysis of the information and data gathered, and exploring new possibilities in the field. 

Broadly speaking, research may be of two types – Quantitative or Qualitative. Quantitative research, also called empirical research, involves the collection of data from sample groups to answer a question. Qualitative research has more to do with the impact of certain phenomena. Such research is usually an extension of previously researched topics. 

The table below highlights the difference between quantitative research topics in business management and qualitative research about business management. 

The world of business management is constantly evolving and finding the right business management research topic might seem like a Herculean task. But, with a little thought, planning, and some research, it is not that hard. So, the 90 topics we've explored in this blog represent some of the most significant areas of development in the field of business management today, from the rise of women as business leaders and to the importance of innovation and network markets. As we move into 2024 and beyond, it's clear that these topics will only continue to grow in importance, shaping the way we do business and interact with the world around us. By staying informed and engaged with the latest research and trends, you can position yourself as a thought leader and innovator in the world of business management. 

Also, our pointers on how to discover a business management research topic will help you identify a list of research topics in business management for your thesis. You can then narrow it down to your area of talent or interest. If you still want to know more, you can enroll in our KnowledgeHut's Business Management training , where you’ll learn more about the different aspects of business. 

Frequently Asked Questions (FAQs)

An example of a business research study could be investigating the impact of social media marketing on consumer buying behavior or examining the effectiveness of a new leadership development program in a company.

The 4 types of business research include:

• Exploratory
• Descriptive

Business management is wide in scope, and there is a spectrum of research topics to choose from. The most prominent areas of business include finance, operations, procurement, marketing, and HR. Within each of these, you’ll find several macro and micro niches to explore.

Mansoor Mohammed

Mansoor Mohammed is a dynamic and energetic Enterprise Agile Coach, P3M & PMO Consultant, Trainer, Mentor, and Practitioner with over 20 years of experience in Strategy Execution and Business Agility. With a background in Avionics, Financial Services, Banking, Telecommunications, Retail, and Digital, Mansoor has led global infrastructure and software development teams, launched innovative products, and enabled Organizational Change Management. As a results-driven leader, he excels in collaborating, adapting, and driving partnerships with stakeholders at all levels. With expertise in Change Management, Transformation, Lean, Agile, and Organizational Design, Mansoor is passionate about aligning strategic goals and delivering creative solutions for successful business outcomes. Connect with him to explore change, Agile Governance, implementation delivery, and the future of work.

Avail your free 1:1 mentorship session.

Something went wrong

Upcoming Business Management Batches & Dates

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 28 January 2021

The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk

• Benjamin J. Singer 1 ,
• Robin N. Thompson 2 , 3 &
• Michael B. Bonsall 1  

Scientific Reports volume  11 , Article number:  2547 ( 2021 ) Cite this article

34k Accesses

23 Citations

199 Altmetric

Metrics details

• Applied mathematics
• Epidemiology

In the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters—such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions—on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

Similar content being viewed by others

Interplay of social distancing and border restrictions for pandemics via the epidemic renormalisation group framework

Second wave COVID-19 pandemics in Europe: a temporal playbook

Regional excess mortality during the 2020 COVID-19 pandemic in five European countries

Introduction.

In the early stages of an infectious disease outbreak, it is important to determine whether the pathogen responsible may go on to cause an epidemic or a pandemic 1 , 2 , 3 , 4 , 5 . There is extensive literature on determining the probability of a major epidemic given a small population of initial infected hosts 6 , 7 , 8 , 9 . This research has produced a natural mathematical definition of an epidemic, based on the bimodal distribution of outbreak sizes given by simple stochastic epidemiological models when \(R_0\) is larger than but not close to one 10 . The term ‘pandemic’ has no corresponding theoretical definition, and there is no consensus mathematical approach to determining the probability of a pandemic. In this study, we examine how alternative definitions of ‘pandemic’ affect quantitative estimates of pandemic risk assessed early in an infectious disease outbreak.

The term ‘pandemic’ is used extensively, appearing in phrases such as ‘pandemic preparedness’ 11 , 12 , 13 , ‘pandemic influenza’ 14 , 15 , 16 , and ‘pandemic potential’ 17 , 18 , 19 . A Google Scholar search returns 25,800 results using the term ‘pandemic’ for 2019 alone.

The International Epidemiology Association’s Dictionary of Epidemiology defines a pandemic as “an epidemic occurring worldwide, or over a very wide area, crossing international boundaries and usually affecting a large number of people” 20 . Notably this definition makes an explicit reference to national borders. Contrastingly, a World Health Organization (WHO) source makes reference to a pandemic as “the worldwide spread of a new disease” 21 .The use of the word ‘new’ here is ambiguous in the context of infectious diseases. HIV/AIDS is often referred to as a global pandemic, but is certainly not new on the timescale of, say, the emergence of influenza strains 22 , 23 . A study by Morens et al. in 2009 finds that there is little in common between all disease outbreaks that have been referred to as pandemics, except that they have a wide geographical extension 24 .

These kinds of differences between pandemic definitions can often go unnoticed, but in certain circumstances they can cause confusion between different stakeholders (e.g. between scientists and governments, or governments and the public), who may not have a shared background understanding of the term. In 2009, the WHO declared a pandemic of H1N1 influenza, using criteria related to the incidence and spread of the virus in different WHO regions 25 . The criteria did not include reference to morbidity or mortality 26 . This fact led to some controversy over whether the declaration of a pandemic was appropriate, as the declaration prompted some governments to mount an intensive response to an outbreak that resulted in fewer yearly deaths than a typical strain of seasonal flu 27 , 28 , 29 , 30 .

International health organisations such as the WHO have not provided any formal definitions of the term ‘pandemic’, and the WHO no longer uses it as an official status of any outbreak 25 , 31 . It would, however, be hasty to dismiss the importance of the term on these grounds. Although the WHO no longer uses the term ‘pandemic’ officially, the WHO Director-General drew attention to their use of the term as recently as March 2020, to describe the status of the COVID-19 outbreak 32 . The Director-General cited “alarming levels of inaction” as one of the reasons to use the term, along with the caveat that “describing the situation as a pandemic does not change WHO’s assessment of the threat posed by this virus”. The WHO’s use of the term was of interest to the public, receiving extensive press coverage 33 , 34 , 35 . The term ‘pandemic’ clearly continues to be important to indicate serious risk during disease outbreaks.

Regardless of the extent to which the pandemic definitions currently in use do or do not agree, they are all qualitative in nature, using descriptions such as “very wide area” and “large number of people”. Perhaps as a result of this, many quantitative studies on pandemics do not make use of a quantitative definition of a pandemic, but instead focus on causally related concepts, such as sustained transmission 19 , or emergence of novel viruses 36 . Others treat the spread of a pathogen at a pandemic level as a context in which to study transmission dynamics, without paying special attention to how those dynamics might lead to a pandemic as distinct from an epidemic or a more limited outbreak 37 , 38 , 39 . In this paper, we examine the effects of alternative pandemic definitions on the analysis of key epidemiological questions. The results provide a foundation for deciding the appropriate quantitative definition of ‘pandemic’ in a given context.

We use a metapopulation model to investigate the effects of pandemic definition on the results of a quantitative assessment of the probability of a pandemic. Metapopulation models are commonly applied to pathogens that spread between regions of the world, and so are appropriate for modelling pandemics 40 , 41 , 42 , 43 , 44 , 45 . We represent states of our metapopulation model as states of a Markov chain, allowing us to calculate the probability of a pandemic directly, as opposed to simulating many stochastic outbreaks and recording the proportion which result in pandemics. We explore two different kinds of pandemic definition, following Morens et al. 2009 24 , specifically:

the family of transregional definitions, where a pandemic is defined as an outbreak in which the number of regions experiencing epidemics meets or exceeds some threshold number n . We refer to specific transregional definitions as n -region transregional definitions, e.g. a 3-region transregional definition.

the interregional definition, where a pandemic is defined as an outbreak in which two or more non-adjacent regions experience epidemics.

Note that these definitions require a specific sense of ‘region’. These regions could be countries, or they could be larger or smaller than individual countries—from counties to health zones to WHO regions. Our metapopulation model (detailed in the Methods section below) can be used to model regions of any size. We have chosen not to include definitions with criteria relating to the number of people infected or killed, instead of, or in addition to, geographical extension. Extension is the only universal factor in pandemic definitions, and so is the focus of the current study 24 .

Three questions that help form public health policy at the beginning of an outbreak are:

Would interventions restricting travel reduce the risk of a pandemic?

Does a portion of the population have pre-existing immunity, and does this affect the risk of a pandemic?

How is the risk of a pandemic affected by regional differences in transmission?

Using our metapopulation model, we explore how changing the pandemic definition does or does not affect our answers to these questions. We show that the precise definition of a pandemic used in modelling studies can (but does not always) affect the inferred risk. The predicted effects of travel restrictions, the influence of pre-existing immunity, and the impact of regional differences in transmission can all vary when alternative definitions of ‘pandemic’ are used. This demonstrates clearly the need to consider carefully the pandemic definition used to assess the risk from an invading pathogen. This is necessary for clear communication of public health risk.

Travel rates

One important question about pandemic risk is what effect inter-regional travel rates have on the probability of a pandemic occurring 16 , 17 , 46 , 47 . Here we model epidemics occurring in regions connected on a network in which the connections and their weighting can be set at fixed values representing the rates of travel between regions. We consider simple networks that can illustrate the effects of our different pandemic definitions—namely, the star network, in which one central region is connected to all others with equal weighting and the non-central regions lack any other connections, and the fully connected network, in which each region is connected to every other with equal weighting. Figure  1 illustrates that the connectivity of the full network is much higher than that of the star network. Using the star network allows us to make the distinction between adjacent and non-adjacent regions, thus allowing us to distinguish between transregional and interregional pandemic definitions.

Illustrations of ( a ) a star network and ( b ) a full network, each with ten regions. Circles represent regions, and straight lines represent travel routes between regions.

Unless otherwise stated, all figures in the current study are generated with a transmission rate of \(\beta = 0.28\) per day, a recovery rate of \(\mu = 0.14\) per day, and an inter-regional travel rate of \(2\times 10^{-4}\) per day. This corresponds to a within-region basic reproduction number ( \(R_0\) ) of 2. These values are within the plausible range for both seasonal and pandemic influenza, and as such they can be used to simulate a plausible pathogen of pandemic potential 38 . We further assume an initial population of 1000 susceptible individuals in each region, and that the outbreak is seeded by a single infectious individual in one region. In the full network, all regions are equivalent, so we seed the outbreak in a single arbitrary region. In the star network, we take the average probability of a pandemic over outbreaks seeded in each region.

Using a model with ten regions allows us to test a range of different transregional definitions of a pandemic. The pandemic probability under a range of n -region transregional definitions for a 10-region network with a variety of travel rates is shown in Fig.  2 . An n -region transregional definition effectively provides a threshold number n —if more than n regions experience epidemics, the outbreak is counted as a pandemic, and otherwise it is not. Thus we indicate the different possible n -region definitions through their threshold numbers in Figs.  2 , 5 , and  6 .

Pandemic probability for a range of between-region travel rates and a range of transregional pandemic definitions. The “pandemic threshold number” refers to the minimum number of regions that must experience epidemics before a pandemic is declared. The pandemic probability is, in general, sensitive to the pandemic definition used, but the degree of sensitivity depends on network structure and travel rates. ( a ) Pandemic probability for a star network. The pandemic probability is, in general, highly sensitive to the pandemic definition used. ( b ) Pandemic probability for a fully connected network. The sensitivity of the pandemic probability to the pandemic definition used is significantly reduced at high travel rates.

The 1-region transregional definition merges the definitions of ‘pandemic’ and ‘epidemic’ in an implausible way, but it is included in these figures for comparison. The comparison between the pandemic probability according to the 2-region definition and according to the 10-region definition shows the difference between pandemic definitions that are satisfied by any transregional transmission and definitions that are satisfied only by truly global spread. For the star network, or for the fully connected network with low travel rates, there is a marked difference between the probability of either of these definitions being satisfied. However, for the fully connected network at medium or high rates of travel, if the pathogen invades the initial region successfully, then it will go on to spread globally. As such, the probability of a pandemic is nears the maximum of 0.5 (i.e. \(1-1/R_0\) ) at all thresholds. For any definition, the probability of a pandemic increases with the connectivity of the network, and with travel rates across the network.

We can also explore the difference in pandemic probability between the transregional and interregional definitions, which make use of a distinction between adjacent and non-adjacent regions. This is shown for a 10-region star network in Fig.  3 a, in which we consider the 2-region transregional and 2-region interregional definitions. We choose a star network as it is one of the simplest network types in which there are adjacent and non-adjacent regions. There is a difference between the 2-region interregional and transregional definitions, but the difference is much smaller than that between the 2-region interregional and 10-region (global) definition, and reduces as travel rates increase. In the case of a fully connected network, all regions are essentially adjacent to each other, so we compare only the 2-region transregional and global definitions. We find that the definitions are clearly distinct for low travel rates, but as the travel rate increases the difference between the likelihood of a pathogen causing an epidemic in one region and the likelihood of it causing epidemics in all regions disappears. This is due to the fact that the pathogen can be introduced into any population from any other.

Plots of pandemic probability against between-region travel rate for a range of pandemic definitions. The difference in probability for different pandemic definitions changes as travel rates increase. ( a ) Plot of pandemic probability for a star network. ( b ) Plot of pandemic probability for a full network. For a fully connected network all regions are adjacent, so no line is shown for the interregional definition, which requires non-adjacent regions to experience epidemics.

In this section we have shown that, when a pandemic is defined in terms of which regions experience epidemics of a disease, different definitions can produce very different estimates of the pandemic probability at low connectivity or travel rates, but have a much smaller effect at high connectivity and travel rates. In the supplementary information, we illustrate that effects due to network structure are mostly due to the difference in motility between the full network and the star network, although topology still plays an important role.

Cross-immunity

Some pathogens with pandemic potential have a prior history of infecting humans, such as pandemic influenza. Newly emerged pathogens with no history of infecting humans are less likely than these established pathogens to encounter regions where susceptible individuals have partial immunity to infection. Established pathogens may encounter individuals with partial immunity acquired from infections with previously circulating strains—i.e. cross-immunity 48 , 49 . It can be important in responding to an outbreak to consider whether any individuals might have existing immunity. We can therefore investigate the interaction between immunity generated by prior exposure and pandemic definition by examining how cross-immunity affects our calculation of the pandemic probability on a network.

We modelled the spread of a pathogen over a ten-region network with no cross-immunity initially, where the initial infected individual could originate in any region. We only included cases where at least one region experienced an epidemic of this initial pathogen. To simulate the emergence of a strain with higher pandemic potential, we then introduced a second pathogen with a higher transmission rate of \(\beta = 0.42\) (corresponding to a basic reproduction number of 3), to which infection with the initial pathogen conferred some degree of partial immunity to infection. The strength of this immunity is written as \(\alpha\) . See the Methods section for details of how cross-immunity is incorporated into our modelling framework. We defined a pandemic as occurring when all ten regions experienced epidemics of the second pathogen, and repeated the model for two values of the level of cross-immunity at a variety of between-region travel rates. The results are presented in Fig.  4 .

Plots of pandemic probability against travel rate for high and low levels of cross-immunity ( \(\alpha\) ) on ten-region networks. A pandemic is defined here as all ten regions experiencing epidemics, i.e. the 10-region transregional definition. The plots show a large relative difference both in the probability of pandemics and in how that probability scales with travel rates for different levels of cross-immunity. The initial infected individual for each outbreak originates in a randomly chosen region. ( a ) Plot of pandemic probability for a star network. ( b ) Plot of pandemic probability for a full network.

First, increasing cross-immunity decreases the probability of a pandemic. Second, the presence of cross-immunity changes how pandemic probability scales with travel rates. In general, the pandemic probability increases faster with travel when the level of cross-immunity is low, except when it reaches a point of saturation as in Fig.  4 b.

Figure  5 shows the simultaneous effects of different n -region transregional pandemic definitions and the degree of cross-immunity in determining the pandemic probability. Here we fix the travel rate at \(2.0 \times 10^{-4}\) per day. In the full network there is a distinct transition from higher risk to lower risk, as cross-immunity approaches one. However, in the star network there is, on average, less circulation of the initial pathogen, so the effect of cross-immunity is less dramatic. Increased cross-immunity can also increase the difference in risk for different pandemic definitions—for the fully connected network, when cross-immunity exceeds \(\alpha = 0.5\) , differences in probability between different thresholds become visible that are much smaller at lower values. This suggests that the probability that an outbreak will develop into a pandemic may be more sensitive to the exact pandemic definition for outbreaks of pathogens that encounter pre-existing immunity than for pathogens which encounter only fully susceptible populations. However, this effect is not seen for the star network, in which the low connectivity of the network results in larger differences in probability between different thresholds even at low levels of cross-immunity.

Pandemic probability for various levels of cross-immunity ( \(\alpha\) ) and a range of transregional pandemic definitions, on a ten-region network. ( a ) Pandemic probability for a star network. ( b ) Pandemic probability for a fully connected network. Here the sensitivity of the pandemic probability to the pandemic definition used increases with cross-immunity, until the probability of any epidemic becomes very low.

Heterogeneous transmission

A topic of great concern during a pandemic is heterogeneity in risk between different countries or regions 50 , 51 . Cross-immunity can create one kind of heterogeneity, since it is common for previous exposure to a pathogen to differ between regions 52 . Another kind of heterogeneity is that due to different public health interventions. Here we ignore cross-immunity and instead examine a heterogeneous fully connected network of ten regions, five of which have a higher rate of transmission of the pathogen than the other five. This can be thought of as an approximation to the difference between poor regions with a relative lack of public health interventions, and wealthy regions with well-funded public health organisations and increased access to healthcare.

The level of heterogeneity was defined as the ratio of the transmission rate in the higher-transmission regions to the transmission rate in the lower-transmission regions. The average transmission rate across all regions was kept fixed at \({\bar{\beta }} = 0.28\) per day, corresponding to a basic reproduction number of 2. The simultaneous effects of heterogeneity and the pandemic definition in determining the pandemic probability are illustrated in Fig.  6 .

Pandemic probability for various degrees of heterogeneity of transmission rates and a range of transregional pandemic definitions, on a fully connected ten-region network where five regions are classed as higher-transmission and the other five regions are classed as lower-transmission. Note that the colour scales differ between the two plots, in order to make the variation in plot ( a ) clearer. ( a ) Pandemic probability for a pathogen emerging in a higher-transmission region. For low thresholds heterogeneity increases the pandemic probability, but at the 10-region threshold the pandemic probability grows and then decreases with increasing heterogeneity. ( b ) Pandemic probability for a pathogen emerging in a lower-transmission region. At all thresholds increasing heterogeneity decreases the pandemic probability.

The row for the 1-region definition shows how the risk of any outbreak varies with the changing basic reproduction number of the pathogen in the region in which it emerges. More complex effects can be seen for higher n -region definitions, especially the 10-region definition, where, at high levels of heterogeneity, even pathogens emerging in higher-transmission regions are prevented from spreading globally due to the low chance of epidemics in lower-transmission regions. Thus the probability of a pandemic under a 10-region definition increases and then decreases with increasing heterogeneity. In the supplementary information, we show that this increasing-decreasing effect exists in networks of different sizes and structures. It appears at different thresholds in different networks. No corresponding effect exists for a pathogen emerging in a lower-transmission region, where increasing heterogeneity always decreases the chance of a pandemic, however it is defined.

In this study, we have developed a theoretical framework to estimate the probability of a pandemic, as detailed in the Methods section below. We use a Markov chain technique based on SIR dynamics to model the spread of a pathogen. The results of this modelling framework reveal in which situations the definition of ‘pandemic’ has a strong effect on the calculated pandemic risk and in which situations it does not. The models also illustrate the effects of differing epidemiological parameters on the pandemic risk under different definitions, and how these effects interact with each other.

Returning to the three epidemiological questions introduced in the introduction, we can see that our results show how the answers can depend on our definition of a pandemic, and on key population and pathogen parameters. The first question was “Would interventions restricting travel reduce the risk of a pandemic?” In Fig.  2 , we see that reductions in travel rates always reduce risk in a network with low connectivity, where travel occurs mainly through a central hub. However, in a highly connected network with high travel rates, travel would have to be extremely highly suppressed to change the probability of a pandemic substantially, under most definitions. This accords with previous findings regarding the effectiveness of restricting travel 53 . Additionally, in the highly connected network, changing the definition of a pandemic makes little difference to the pandemic probability, for high enough values of the travel rate.

Figure  3 further illustrates the effects of different definitions. Changing the pandemic definition can sometimes greatly alter the estimated probability of a pandemic, as seen in Fig.  3 a between the yellow line, representing the 2-region transregional definition, and the purple line, representing the 10-region transregional definition. The effect on the pandemic risk of reducing travel rates also differs substantially between these two definitions. However, there are situations where changing the definition does not significantly change the pandemic probability, as seen in the same figure between the yellow line and the dashed green line, representing the 2-region interregional definition. Both the estimated risk and the effect of reducing travel are very similar in these two cases. So, while some changes in definition do not cause a large change in quantitative analyses of the risk of a pandemic, others may significantly alter both our point estimates and the predicted effects of key parameters. Figure  3 b shows that this may depend on the values of those key parameters themselves. For low travel rates, the pandemic probability is very different for the two illustrated definitions, but at high travel rates the pandemic probabilities for the two definitions converge.

The second question was “Does a portion of the population have pre-existing immunity, and does this affect the risk of a pandemic?” The presence of immunity can significantly alter the results discussed in the paragraphs above. In Fig.  5 b, the leftmost column is equivalent to the column from Fig.  2 b in which \(\lambda = 2.0 \times 10^{-4}\) per day, but with a higher transmission rate of \(\beta =0.42\) . However, as cross-immunity increases, a marked difference in the pandemic probability between different definitions becomes visible. This shows that the conclusion that precise pandemic definitions are of reduced importance in a highly connected network with high travel rates is context sensitive—if the population has high immunity, differences between definitions re-emerge.

The third question was “How is the risk of a pandemic affected by differences between regions?” In Fig.  6 , we examined how heterogeneous transmission rates in different regions affect the pandemic probability. Many pathogens have higher transmission rates in lower income countries, and novel pathogens are more likely to emerge in low income countries 50 , 51 , 54 . Putting these two facts together, we see that pathogens are most likely to emerge in countries in which they have higher transmission rates. Motivated by this, we compared the scenarios of emergence in a higher-transmission and lower-transmission region, finding that pandemic definition makes a larger difference for diseases emerging in a higher-transmission region. In particular, when the pandemic definition requires many countries to experience epidemics to qualify an outbreak as a pandemic, including countries with lower transmission rates, we see striking non-linearity in the relationship between heterogeneity and the pandemic probability. For these definitions, as the difference in transmission rates between higher- and lower-transmission regions increases, the pandemic probability increases initially, before decreasing. This initial rise is due to the enhanced spread between high-transmission regions increasing the importation rate to low-transmission regions. This result implies that, when the mean value of the transmission rate is fixed, a small gap in the effectiveness of public health infrastructure between wealthy and poor regions puts all regions at greater risk, while a larger gap protects wealthier regions while the risk for poor regions continues to increase.

To illustrate this concept, consider the contrasting examples of Ebola and COVID-19. The 2014 outbreak of Ebola virus followed the pattern of high incidence in low income countries but low incidence in high income countries. The virus spread through several low-income African countries but was effectively contained when introduced to high-income countries 55 , 56 , 57 . In this case, high-income countries had the capacity to prevent a pandemic from taking hold, being able to quickly isolate and treat symptomatic individuals. This generated high heterogeneity in transmission, corresponding to the right side of Fig.  6 a, with low-income countries at high risk and high-income countries at low risk. In contrast, high-income countries have not been able to escape the pandemic of COVID-19, in part due to asymptomatic and presymptomatic transmission of SARS-CoV-2 allowing it to evade surveillance and public health measures 58 , 59 . This has led to more similar transmission rates between different countries, corresponding to the left side of Fig.  6 a, where risk is more uniform between regions and therefore between pandemic definitions.

In our analyses, we use a metapopulation modelling framework. Metapopulation models are widely used in pandemic modelling 40 , 41 , 42 , 43 , 44 , 45 . Our novel Markov chain approach allows us to calculate pandemic probabilities directly, without requiring large numbers of simulations to generate an approximation. We expect our overall conclusion, that the effects of key parameters on pandemic risk depend on the pandemic definition, to hold irrespective of the underlying modelling framework. Future studies could replicate our analyses using different models and modelling approaches, such as metapopulation models with additional epidemiological complexity 43 , 45 , 60 , 61 or the widely used global epidemic and mobility (GLEaM) model 62 , 63 , 64 . Exploring how our quantitative results vary for different modelling frameworks in the field of mathematical epidemiology 14 , 16 , 65 , 66 , 67 is a target for further investigation.

Other future work using our modelling framework could address the role of pandemic definitions in quantifying the effects of additional epidemiological parameters on pandemic risk, such as use of different types of travel (e.g. within-country transport or international flights) 45 , 68 , 69 , the rate of nosocomial infections 70 , or age structure 71 . Our metapopulation modelling framework is generally applicable, and this framework could be extended to represent outbreaks of many different specific pathogens emerging in various locations. An important factor for response planning is the timescale over which outbreaks develop into pandemics. The duration of the initial phase of outbreaks has been a subject of previous study 72 , as has the overall duration of outbreaks 10 , 73 , 74 , 75 , 76 . In theory, Markov chain models could be used to assess the time for a local epidemic to develop into a pandemic, and we leave this as an avenue for further work.

In summary, we have developed a novel modelling framework for estimating the pandemic risk. We have applied this framework to assess the pandemic risk in a range of different scenarios, and have interpreted the results under a variety of pandemic definitions. We have found that certain relationships, such as the effect of heterogeneity in transmission between regions on the risk of a pandemic, are highly dependent on the definition of ‘pandemic’ used, while others, such as the effect of high travel rates on pandemic risk in a highly connected network, are not. This work provides a foundation for improved communication about pandemic risk, by highlighting the contexts in which pandemic definitions need to be provided in quantitative detail. In general, we contend that, when assessing the risk that an outbreak will develop into a pandemic, the precise pandemic definition used for a given analysis should be considered and stated clearly. Future work could investigate the effects of alternative definitions in more detailed epidemiological models, and extend this framework to investigate different dynamical features of pandemics.

We have combined standard epidemiological modelling techniques with a novel Markov chain treatment of metapopulation dynamics to produce a method for calculating the probabilities of epidemics and pandemics in a network of population regions. At each step of this chain, we resolve information about which regions may experience epidemics. The order in which the status of any given region is resolved does not necessarily match the order in which the given epidemics occur in calendar time. A benefit of our model is that we can calculate the probabilities of different final outcomes directly, without requiring large numbers of stochastic simulations to estimate these values. This comes at the cost that temporal information is not represented explicitly in our model: we focus on the pandemic probability, accounting for all possible ways that a pandemic could occur, rather than estimating the possible times at which epidemics could occur in different regions or the timescale over which an outbreak will develop into a pandemic (see Discussion).

We model the transmission of a pathogen through n regions labelled \(P_1, P_2, P_3, \ldots , P_n\) . Each region \(P_j\) has associated with it some intra-region pathogen transmissibility \(\beta _j\) , disease recovery rate \(\mu _j\) , and population size \(N_j\) . From these quantities it is possible to calculate a region-specific basic reproduction number \(R_{0,j}\) . This can be fixed across all regions for a particular pathogen, or allowed to vary from region to region to reflect local epidemiological differences.

First let us consider the spread of the pathogen in a single region, using well-established results of stochastic Susceptible-Infected-Recovered (SIR) models. If a region \(P_j\) contains an initial number of infected individuals \(I_j(0)\) , then in the stochastic SIR model, the probability that these individuals do not cause an epidemic in \(P_j\) is \((1/R_{0,j})^{I_j(0)}\) when \(R_{0,j}\ge 1\) , and 1 otherwise 17 . We also define the final size of an epidemic \(R_{j}(\infty )\) (not to be confused with \(R_{0,j}\) ) as the number of recovered individuals in \(P_j\) at the end of the epidemic. This equals the total number of individuals in \(P_j\) who become infected at any time, and is given by the solution of the following equation 77 .

Infected individuals are assumed to travel from region \(P_j\) to region \(P_m\) at a rate \(\lambda _{jm}\) . We seek the probability that infected individuals travelling from \(P_j\) will not cause an epidemic in \(P_m\) , in the case where initially infected individuals in \(P_m\) do not cause an epidemic in \(P_m\) (including the case where there are no initially infected individuals in \(P_m\) ). This is equal to the probability that i infected individuals migrate from \(P_j\) to \(P_m\) , multiplied by the probability that this number of individuals fails to cause a major epidemic, summed over possible values of i . The minimum value of i is the case where no infected individuals migrate, and the maximum value is the case where all individuals in \(P_j\) that become infected at any point migrate. This gives us an expression for \(q_{jm}\) , the conditional probability that, if \(P_j\) experiences an epidemic and \(P_m\) does not experience an epidemic due to a source of infected individuals other than \(P_j\) , \(P_m\) does not experience an epidemic.

This approximation is valid when the number of infected individuals that travel between regions is much smaller than the size of the regions.

We assume that infected individuals travelling from a region \(P_j\) cannot cause an epidemic in a neighbouring region \(P_m\) if \(P_j\) does not itself experience an epidemic. Then computing the value of \(q_{jm}\) for every pair of populations \(P_j\) and \(P_m\) gives us sufficient information to determine the probability of any particular set of regions connected on a network experiencing epidemics so long as there are no interactions between different groups of migrants arriving in a region, and the total numbers of migrants in any region remains very small relative to the region’s size. If these assumptions hold, we can imagine the regions on a network with weighted directed edges, where the weight of the edge directed from region \(P_j\) to region \(P_m\) is \(q_{jm}\) .

To determine how the final probabilities of epidemics depend on the pairwise probabilities \(q_{jm}\) , we use a Markov chain. The states of this Markov chain assign one of three states to each region— N (for neutral), where it is not yet determined whether the region will experience an epidemic, E (for epidemic), where it is determined that the region will experience an epidemic but it is not yet determined in which further regions it will cause epidemics, and T (for terminal), where it is determined that the region will experience an epidemic and in which further regions it will cause epidemics due to onward transmission. As our model does not explicitly represent dynamical processes occurring over time, these states should not be interpreted as actual states of infection and recovery within regions, but rather as bookkeeping devices for the role of various regions in determining the spread of the pathogen through the network.

Suppose we have a network connecting n regions. In the initial state, each region where the initially infected individuals have caused an epidemic is in state E , and all the other regions are in state N . The global state of the network is simply the product of the states of each system. We can then define a transition matrix \(\mathbf{T }\) that acts on the global state. The elements of this matrix are denoted \(t_{x_1x_2\ldots x_n \rightarrow y_1y_2\ldots y_n}\) .

\(x_j\) is the state ( N , E , or T ) of region \(P_j\) before the transition, and \(y_j\) is its state afterwards. The expression inside the first set of square brackets ensures that the only acceptable transitions for any given region are \(N \rightarrow E\) and \(E \rightarrow T\) , and requires that all epidemic regions in the initial state must be terminated in the transition (this prevents double-counting of possible transmission paths). The expression inside the second set of square brackets gives the probability of each \(N \rightarrow E\) transition, and the expression inside the final set of square brackets gives the probability of each \(N \rightarrow N\) transition, given the regions that are in state E before the transition.

Note that these transitions do not represent a dynamical process—the order of transitions in this model does not necessarily correspond to the order in which regions experience epidemics. Instead, the transitions are simply stages along the exploration of different routes and outcomes from the disease spreading process.

The initial probability of each global state \(z_1z_2\ldots z_n\) (where \(z_i \in \{N, E, T\}\) ) is given by:

where \(Q_j = \min ((1/R_{0,j})^{I_j(0)},1)\) is the probability that the initial population of infective individuals does not cause an epidemic in region \(P_j\) . Essentially, no region can begin in state T , and the probability of each initial global state is given by the product of the probabilities of each region being in the corresponding initial regional state.

In this system, all states in which no region is epidemic are absorbing, and in each transition at least one epidemic state must become terminal. This means that the system must reach an absorbing state in at most n transitions, since at least one region becomes terminal in each transition, and a fully terminal state is absorbing. So the final probability vector \(p_{\mathrm {final}}\) is given by

with \({\mathbf{T}}\) as the transition matrix and \(p_{\mathrm {initial}}\) as the vector whose elements defined by Eq. ( 5 ). This final vector gives the probabilities of each configuration of the metapopulation, with populations in state N never experiencing an epidemic, and regions in state T experiencing an epidemic at some point.

Cross-Immunity

The model described above can incorporate certain epidemiological details, such as heterogeneity of population parameters, but is restricted to treating quite simple disease dynamics. In this section we expand the model to treat pathogens that give those who overcome infection cross-protection against future strains of that pathogen. This is necessary to be able to investigate how pre-existing immunity changes how pandemic definitions affect the results of our model.

We first describe the spread of a pathogen strain X using the methods above, introducing a superscript X to the relevant parameters to mark the strain, e.g. \(R_0^X\) , \(R^X(\infty )\) , and \(p^X_{\mathrm {final}}\) . We assume that infection with pathogen X confers cross-immunity \(\alpha\) to a second strain of the pathogen, which we call Y . In each population \(P_j\) we can define an effective basic reproductive number for Y in the case that \(P_j\) has experienced an epidemic of X , which we call \(R^Y_{e,j}\) .

This expression simply multiplies the basic reproductive number by the effective number of susceptible individuals given the prevalence of cross-immunity in the population. It is through this expression that cross-immunity enters the model—the parameter \(\alpha\) does not otherwise appear in what follows.

We can write down an equation for the expected total number of individuals in \(P_j\) infected in an epidemic of Y in analogy to Eq. ( 1 ). In the case where there has been no previous epidemic of X in \(P_j\) , the expected epidemic size is the solution \(R_{j,\mathrm{no}X}^Y(\infty )\) of

In the case where there has been a previous epidemic of X in \(P_j\) , the expected epidemic size is the solution \(R_{j,X}^Y(\infty )\) of

We assume that individuals infected with Y travel at the same rate as individuals infected with X . We then define the pairwise probabilities of transmission of Y between populations in analogy to Eq. ( 2 ). That is,

where \(R^Y_{c,m} = R^Y_{0,m}\) when \(P_m\) has not experienced a previous epidemic of X , \(R^Y_{c,m} = R^Y_{e,m}\) when \(P_m\) has experienced a previous epidemic of X , \(R^Y_{j,b}(\infty ) = R^Y_{j,\mathrm{no}X}(\infty )\) when \(P_j\) has not experienced a previous epidemic of X , and \(R^Y_{j,b}(\infty ) = R^Y_{j,X}(\infty )\) when \(P_j\) has experienced a previous epidemic of X .

These expressions for \(q^Y_{jm}\) can be substituted for \(q_{jm}\) in Eq. ( 3 ) to yield a transition matrix for modelling the spread of Y , which we will call \({\mathbf{T}}^Y(s_1s_2\ldots s_n)\) , where \(s_j\) is the final state (either N or T ) of the X outbreak in \(P_j\) . We find the initial probabilities of each state with regards to Y , \(p^Y_{\mathrm {initial}}\) , in analogy to Eq. ( 5 ), given an initial number of individuals infected with Y in each population \(I^Y_j(0)\) .

where \(Q^Y_j = \min [(1/R^Y_{0,j})^{I^Y_j(0)},1]\) when \(P_j\) has not experienced a previous epidemic of X (i.e. \(s_j=N\) ), and \(Q^Y_j = \min [(1/R^Y_{e,j})^{I^Y_j(0)},1]\) when \(P_j\) has experienced a previous epidemic of X (i.e \(s_j = T\) ). We can then write the final probabilities of each combination of possible epidemics of Y , for a given set of previous epidemics of X , as

To find the overall probability of each combination of epidemics of Y in various populations given a prior probability of each combination of epidemics of X (given by \(p^X_{\mathrm {final}}(s_1s_2\ldots s_n)\) defined in Eq. ( 6 )), we sum over the possible values of \((s_1s_2\ldots s_n)\) , weighted by their probability.

Code availability

Code is available on the Open Science Framework at https://osf.io/z52te/ .

Craft, M. E., Beyer, H. L. & Haydon, D. T. Estimating the probability of a major outbreak from the timing of early cases: an indeterminate problem?. PLoS ONE 8 , 1–7. https://doi.org/10.1371/journal.pone.0057878 (2013).

Article   CAS   Google Scholar  

Thompson, R. N., Gilligan, C. A. & Cunniffe, N. J. Detecting presymptomatic infection is necessary to forecast major epidemics in the earliest stages of infectious disease outbreaks. PLoS Comput. Biol. 12 , 1–18. https://doi.org/10.1371/journal.pcbi.1004836 (2016).

Jamison, D. T. et al. (eds.) Disease Control Priorities: Improving Health and Reducing Poverty , vol. 9 (World Bank, Washington (DC), 2018), 3rd edn. https://doi.org/10.1016/S0140-6736(15)60097-6 .

Adalja, A. A., Watson, M., Toner, E. S., Cicero, A. & Inglesby, T. V. Characteristics of microbes most likely to cause pandemics and global catastrophes. Curr. Top. Microbiol. Immunol. https://doi.org/10.1007/82_2019_176 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Thompson, R. N. Novel coronavirus outbreak in Wuhan, China, 2020: intense surveillance is vital for preventing sustained transmission in new locations. J. Clin. Med. 9 , 498. https://doi.org/10.3390/jcm9020498 (2020).

Article   PubMed Central   Google Scholar  

Tarwater, P. M. & Martin, C. F. Effects of population density on the spread of disease. Complexity 6 , 29–36. https://doi.org/10.1002/cplx.10003 (2001).

Article   MathSciNet   Google Scholar  

Miller, J. C. Spread of infectious disease through clustered populations. J. R. Soc. Interface 6 , 1121–34 (2009).

Article   Google Scholar  

House, T., Ross, J. V. & Sirl, D. How big is an outbreak likely to be? Methods for epidemic final-size calculation. Proc. R. Soc. A Math. Phys. Eng. Sci. 469 , 20120436–20120436 (2012).

Ball, F., Sirl, D. & Trapman, P. Threshold behaviour and final outcome of an epidemic on a random network with household structure. Adv. Appl. Probability 41 , 765–796 (2009).

Thompson, R., Gilligan, C. & Cunniffe, N. Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic. J. R. Soc. Interface https://doi.org/10.1098/rsif.2020.0690 (2020).

Kyoon-achan, G. & Wright, L. Community-based pandemic preparedness: COVID-19 procedures of a Manitoba First Nation community. J. Community Saf. Well-Being 5 , 45–50 (2020).

Marston, H. D., Paules, C. I. & Fauci, A. S. The critical role of biomedical research in pandemic preparedness. JAMA 318 , 1757 (2017).

Monto, A. S., Comanor, L., Shay, D. K. & Thompson, W. W. Epidemiology of pandemic influenza: use of surveillance and modeling for pandemic preparedness. J. Infect. Dis. 194 , 92–97 (2006).

Lunelli, A., Pugliese, A. & Rizzo, C. Epidemic patch models applied to pandemic influenza: contact matrix, stochasticity, robustness of predictions. Math. Biosci. 220 , 24–33. https://doi.org/10.1016/j.mbs.2009.03.008 (2009).

Article   MathSciNet   PubMed   MATH   Google Scholar  

Chowell, G., Nishiura, H. & Bettencourt, L. M. Comparative estimation of the reproduction number for pandemic influenza from daily case notification data. J. R. Soc. Interface 4 , 154–166. https://doi.org/10.1098/rsif.2006.0161 (2007).

Colizza, V., Barrat, A., Barthelemy, M., Valleron, A. J. & Vespignani, A. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS Med. 4 , 95–110. https://doi.org/10.1371/journal.pmed.0040013 (2007).

Thompson, R. N., Thompson, C. P., Pelerman, O., Gupta, S. & Obolski, U. Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2018.0274 (2019).

Thompson, R. Pandemic potential of 2019-nCoV. Lancet Infect. Dis. 3099 , 30068. https://doi.org/10.1016/S1473-3099(20)30068-2 (2020).

Breban, R., Riou, J. & Fontanet, A. Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk. The Lancet 382 , 694–699. https://doi.org/10.1016/S0140-6736(13)61492-0 (2013).

Porta, M. A Dictionary of Epidemiology (Oxford University Press, USA, 2008). https://www.oxfordreference.com/view/10.1093/acref/9780195314496.001.0001/acref-9780195314496 .

World Health Organization. What is a pandemic? (2010). https://www.who.int/csr/disease/swineflu/frequently_asked_questions/pandemic/en/ .

Osman, A. S. HIV / AIDS in the last 10 years. Eastern Mediterranean Health Journal 14 , 90–96 (2007). http://www.emro.who.int/emhj-volume-14-2008/volume-14-supplement/hivaids-in-the-last-10-years.html .

Cohen, M. S. et al. The spread, treatment, and prevention of HIV-1: evolution of a global pandemic. J. Clin. Investig. 118 , 1244–1254. https://doi.org/10.1172/JCI34706.1244 (2008).

Article   CAS   PubMed   Google Scholar  

Morens, D., Folkers, G. & Fauci, A. S. What is a pandemic?. JAMA J. Am. Med. Assoc. 321 , 910. https://doi.org/10.1001/jama.2019.0700 (2009).

Doshi, P. The elusive definition of pandemic influenza. Bull. World Health Organ. 89 , 532–538. https://doi.org/10.2471/BLT.11.086173 (2011).

Coninx, J. K. et al. (eds.) Pandemic influenza preparedness and response: a WHO guidance document (WHO Press, 2009). https://www.who.int/influenza/resources/documents/pandemic_guidance_04_2009/en/ .

Bonneux, L. & Van Damme, W. Health is more than influenza. Bull. World Health Organ. 89 , 539–540. https://doi.org/10.2471/BLT.11.089078 (2011).

Simonsen, L. et al. Global mortality estimates for the 2009 influenza pandemic from the GLaMOR project: a modeling study. PLoS Med. https://doi.org/10.1371/journal.pmed.1001558 (2009).

Coronavirus Disease (COVID-19) Press Conference 17 February 2020 (2020). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/media-resources/press-briefings .

Thompson, R. N., Gilligan, C. A. & Cunniffe, N. J. Control fast or control smart: when should invading pathogens be controlled?. PLoS Comput. Biol. 14 , 1–21. https://doi.org/10.1371/journal.pcbi.1006014 (2018).

Nebehay, S. WHO says it no longer uses ’pandemic’ category, but virus still emergency (2020). https://www.reuters.com/article/uk-china-health-who/who-says-it-no-longer-uses-pandemic-category-but-virus-still-emergency-idUKKCN20I0PD .

World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020 (2020). https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 .

Kopecki, D., Lovelace Jr., B., Feuer, W. & Higgins-Dunn, N. WHO declares the coronavirus outbreak a global pandemic (2020). https://www.cnbc.com/2020/03/11/who-declares-the-coronavirus-outbreak-a-global-pandemic.html .

Boseley, S. WHO declares coronavirus pandemic (2020). https://www.theguardian.com/world/2020/mar/11/who-declares-coronavirus-pandemic .

Wan, W. What is pandemic? Why did WHO just declare one? (2020). https://www.washingtonpost.com/health/2020/03/11/who-declares-pandemic-coronavirus-disease-covid-19/ .

Webster, R. G. Predictions for future human influenza pandemics. J. Infect. Dis. 176 , S14–S19. https://doi.org/10.1086/514168 (1997).

Article   PubMed   Google Scholar  

Towers, S. & Feng, Z. Pandemic H1N1 influenza: predicting the course of a pandemic and assessing the efficacy of the planned vaccination programme in the United States. Eurosurveillance 14 , 1–3 (2009).

Coburn, B. J., Wagner, B. G. & Blower, S. Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1). BMC medicine 7 (2009). http://www.ncbi.nlm.nih.gov/pubmed/19545404 .

Chowell, G., Bettencourt, L. M., Johnson, N., Alonso, W. J. & Viboud, C. The 1918–1919 influenza pandemic in England and Wales: spatial patterns in transmissibility and mortality impact. Proc. R. Soc. B Biol. Sci. 275 , 501–509. https://doi.org/10.1098/rspb.2007.1477 (2008).

Apolloni, A., Poletto, C., Ramasco, J. J., Jensen, P. & Colizza, V. Metapopulation epidemic models with heterogeneous mixing and travel behaviour. Theor. Biol. Med. Model. 11 , 1–26. https://doi.org/10.1186/1742-4682-11-3 (2014).

Hufnagel, L., Brockmann, D. & Geisel, T. Forecast and control of epidemics in a globalized world. Proc. Natl. Acad. Sci. 101 , 15124–15129. https://doi.org/10.1073/pnas.0308344101 (2004).

Article   ADS   CAS   PubMed   Google Scholar  

Watts, D. J., Muhamad, R., Medina, D. C. & Dodds, P. S. Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proc. Natl. Acad. Sci. 102 , 11157–11162. https://doi.org/10.1073/pnas.0501226102 (2005).

Colizza, V., Barrat, A., Barthelemy, M. & Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl. Acad. Sci. 103 , 2015–2020. https://doi.org/10.1073/pnas.0510525103 (2006).

Article   ADS   CAS   PubMed   MATH   Google Scholar  

Ball, F. et al. Seven challenges for metapopulation models of epidemics, including households models. Epidemics 10 , 63–67 (2015).

Pei, S., Kandula, S., Yang, W. & Shaman, J. Forecasting the spatial transmission of influenza in the United States. Proc. Natl. Acad. Sci. U. S. A. 115 , 2752–2757. https://doi.org/10.1073/pnas.1708856115 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Brownstein, J. S., Wolfe, C. J. & Mandl, K. D. Empirical evidence for the effect of airline travel on inter-regional influenza spread in the United States. PLoS Med. 3 , 1826–1835. https://doi.org/10.1371/journal.pmed.0030401 (2006).

Mateus, A. L., Otete, H. E., Beck, C. R., Dolan, G. P. & Nguyen-Van-Tam, J. S. Effectiveness of travel restrictions in the rapid containment of human influenza: a systematic review. Bull. World Health Organ. https://doi.org/10.2471/BLT.14.135590 (2014).

Andreasen, V. Dynamics of annual influenza A epidemics with immuno-selection. J. Math. Biol. 46 , 504–536. https://doi.org/10.1007/s00285-002-0186-2 (2003).

Valkenburg, S. A. et al. Immunity to seasonal and pandemic influenza A viruses. Microbes Infect. 13 , 489–501. https://doi.org/10.1016/j.micinf.2011.01.007.Immunity (2013).

Walker, P. G. T. et al. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 422 , 413–422. https://doi.org/10.1126/science.abc0035 (2020).

Article   ADS   CAS   Google Scholar  

Bonds, M. H., Keenan, D. C., Rohani, P. & Sachs, J. D. Poverty trap formed by the ecology of infectious diseases. Proc. R. Soc. B Biol. Sci. 277 , 1185–1192. https://doi.org/10.1098/rspb.2009.1778 (2010).

Penman, B. S., Gupta, S. & Shanks, G. D. Rapid mortality transition of Pacific Islands in the 19th century. Epidemiol. Infect. 145 , 1–11. https://doi.org/10.1017/S0950268816001989 (2017).

Hossain, M. P. et al. The effects of border control and quarantine measures on the spread of COVID-19. Epidemics. https://doi.org/10.1016/j.epidem.2020.100397 (2020).

Jones, K. E. et al. Global trends in emerging infectious diseases HHS Public Access. Nature 451 , 990–993. https://doi.org/10.1038/nature06536 (2008).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Merino, J. G. Response to Ebola in the US: misinformation, fear, and new opportunities. BMJ (Online) 349 , 1–2. https://doi.org/10.1136/bmj.g6712 (2014).

World Health Organization. Ebola virus disease - United Kingdom (2014). https://www.who.int/csr/don/30-december-2014-ebola/en/ .

Gomes, M. F. C. et al. Assessing the international spreading risk associated with the 2014 West African ebola outbreak. PLoS Curr. Outbreaks https://doi.org/10.1371/currents.outbreaks.cd818f63d40e24aef769dda7df9e0da5 (2014).

Gandhi, M., Yokoe, D. S. & Havlir, D. V. Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19. N. Engl. J. Med. 382 , 2158–2160. https://doi.org/10.1056/NEJMe2009758 (2020).

Lai, C. C., Shih, T. P., Ko, W. C., Tang, H. J. & Hsueh, P. R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int. J. Antimicrob. Agents 55 , 105924. https://doi.org/10.1016/j.ijantimicag.2020.105924 (2020).

Meakin, S. R., Tildesley, M. J., Davis, E. & Keeling, M. J. A metapopulation model for the 2018 Ebola outbreak in Equateur province in the Democratic Republic of the Congo. bioRxiv 1–30 (2018). https://doi.org/10.1101/465062 .

Baguelin, M. et al. Control of equine influenza: Scenario testing using a realistic metapopulation model of spread. J. R. Soc. Interface 7 , 67–79. https://doi.org/10.1098/rsif.2009.0030 (2009).

Balcan, D. et al. Multiscale mobility networks and the spatial spreading of infectious diseases. Proc. Natl. Acad. Sci. U. S. A. 106 , 21484–21489. https://doi.org/10.1073/pnas.0906910106 (2009).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Bajardi, P. et al. Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic. PLoS ONE https://doi.org/10.1371/journal.pone.0016591 (2011).

Gonçalves, B., Balcan, D. & Vespignani, A. Human mobility and the worldwide impact of intentional localized highly pathogenic virus release. Sci. Rep. 3 , 1–7. https://doi.org/10.1038/srep00810 (2013).

Bosch, F. V. D., Metz, J. A. J. & Zadoks, J. C. Pandemics of focal plant disease, a model. Anal. Theor. Plant Pathol. https://doi.org/10.1094/PHYTO.1999.89.6.495 (1999).

Chao, D. L., Halloran, M. E., Obenchain, V. J. & Longini, I. M. FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1000656 (2010).

Article   MathSciNet   PubMed   PubMed Central   Google Scholar  

Thompson, R. N. & Brooks-Pollock, E. Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants (Trans. R. Soc. B Biol. Sci, Philos, 2019).

Google Scholar  

Khan, K. et al. Infectious disease surveillance and modelling across geographic frontiers and scientific specialties. Lancet Infect. Dis. 12 , 222–230. https://doi.org/10.1016/S1473-3099(11)70313-9 (2012).

Poletto, C., Tizzoni, M. & Colizza, V. Human mobility and time spent at destination: impact on spatial epidemic spreading. J. Theor. Biol. 338 , 41–58. https://doi.org/10.1016/j.jtbi.2013.08.032 (2013).

Barbarossa, M. V. et al. Transmission dynamics and final epidemic size of ebola virus disease outbreaks with varying interventions. PLoS ONE 10 , 1–21. https://doi.org/10.1371/journal.pone.0131398 (2015).

Nishiura, H., Chowell, G., Safan, M. & Castillo-Chavez, C. Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009. Theor. Biol. Med. Model. 7 , 1–13. https://doi.org/10.1186/1742-4682-7-1 (2010).

Miller, J. C., Davoudi, B., Meza, R., Slim, A. C. & Pourbohloul, B. Epidemics with general generation interval distributions. J. Theor. Biol. 262 , 107–115. https://doi.org/10.1016/j.jtbi.2009.08.007 (2010).

van Herwaarden, O. A. & Grasman, J. Stochastic epidemics: major outbreaks and the duration of the endemic period. J. Math. Biol. 33 , 581–601. https://doi.org/10.1007/BF00298644 (1995).

Article   PubMed   MATH   Google Scholar  

Grasman, J. Stochastic epidemics: the expected duration of the endemic period in higher dimensional models. Math. Biosci. 152 , 13–27. https://doi.org/10.1016/S0025-5564(98)10020-2 (1998).

Article   MathSciNet   CAS   PubMed   MATH   Google Scholar  

Barbour, A. D. The duration of the closed stochastic epidemic. Biometrika 62 , 477–482. https://doi.org/10.1093/biomet/62.2.477 (1975).

Article   MathSciNet   MATH   Google Scholar  

Allen, L. J. & Allen, E. J. A comparison of three different stochastic population models with regard to persistence time. Theor. Popul. Biol. 64 , 439–449. https://doi.org/10.1016/S0040-5809(03)00104-7 (2003).

Miller, J. C. A note on the derivation of epidemic final sizes. Bull. Math. Biol. 74 , 1. https://doi.org/10.1007/s11538-012-9749-6 (2012).

Download references

Acknowledgements

This work was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. This research was funded by Christ Church, Oxford, via a Junior Research Fellowship (RNT).

Author information

Authors and affiliations.

Department of Zoology, University of Oxford, Oxford, UK

Benjamin J. Singer & Michael B. Bonsall

Christ Church, University of Oxford, Oxford, UK

Robin N. Thompson

Mathematical Institute, University of Oxford, Oxford, UK

You can also search for this author in PubMed   Google Scholar

Contributions

B.J.S., R.N.T., and M.B.B. conceived the study. B.J.S. carried out the analysis, wrote the manuscript, and prepared the figures. R.N.T. and M.B.B. supervised the research. All authors revised the manuscript and gave final approval for submission.

Corresponding author

Correspondence to Benjamin J. Singer .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary figures., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Singer, B.J., Thompson, R.N. & Bonsall, M.B. The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk. Sci Rep 11 , 2547 (2021). https://doi.org/10.1038/s41598-021-81814-3

Download citation

Received : 04 September 2020

Accepted : 29 December 2020

Published : 28 January 2021

DOI : https://doi.org/10.1038/s41598-021-81814-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

The incidence of psychosocial disturbances during the coronavirus disease-19 pandemic in an iranian sample.

• Azam Farmani
• Mojtaba Rahimian Bougar
• Hooman Farahmand

Current Psychology (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

The Drivers of Post-Pandemic Inflation

Post-covid inflation was predominantly driven by unexpectedly strong demand forces, not only in the United States, but also in the Euro Area. In comparison, the inflationary impact of adverse supply shocks was less pronounced, even though these shocks significantly constrained economic activity. With output already weakened by these unfavourable supply conditions, any attempt by the European Central Bank to further mitigate the demand-driven inflationary pressures---to maintain inflation near its 2-percent target---would have severely hampered an already anaemic recovery.

We thank our discussant, Fernanda Nechio, an anonymous ECB referee, Philipp Hartmann, Jirka Slacalek, Carlo Altavilla, Giacomo Carboni, Jacopo Cimadomo, Chris Erceg, Pierre-Olivier Gourinchas, Davide Furceri, Kamil Koval, Michele Lenza, Matteo Luciani, Alberto Musso, Mario Porqueddu, Massimo Rostagno and Antonio Spilimbergo for helpful comments and discussions. Domenico Giannone started working on this project before joining the IMF. The views expressed here are those of the authors and do not necessarily represent those of the National Bureau of Economic Research, IMF, its Management and Executive Board, IMF policy.

Non-teaching compensated activities, 2017-2020: American Economic Journal: Macroeconomics, co-editor, Federal Reserve Bank of Chicago, consultant European Central Bank, consultant.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

A quantitative and qualitative analysis of the COVID-19 pandemic model

Affiliations.

• 1 Department of Mathematics, University of Raparin, Ranya, Sulaimani, Iraq.
• 2 Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan.
• PMID: 32523257
• PMCID: PMC7247488
• DOI: 10.1016/j.chaos.2020.109932

Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

Keywords: Computational simulations; Coronavirus disease (COVID-19); Mathematical modeling; Model reduction; Sensitivity analysis.

© 2020 Elsevier Ltd. All rights reserved.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no competing interests.

The model interaction individuals for…

The model interaction individuals for the COVID–19 epidemic outbreak with reaction rates.

Computational simulations for the model…

Computational simulations for the model states given in system (9) of the COVID–19…

The effect of transition rate…

The effect of transition rate δ on (a) asymptomatic infected individuals, (b) unreported…

The effect of transition rate γ on (a) asymptomatic infected people, (b) unreported…

The effect of parameter η…

The effect of parameter η on (a) unreported symptomatic infected people, (b) reported…

The sensitivity of each model…

The sensitivity of each model state concerning model parameters in computational simulations for…

Similar articles

• Optimal control problem arising from COVID-19 transmission model with rapid-test. Aldila D, Shahzad M, Khoshnaw SHA, Ali M, Sultan F, Islamilova A, Anwar YR, Samiadji BM. Aldila D, et al. Results Phys. 2022 Jun;37:105501. doi: 10.1016/j.rinp.2022.105501. Epub 2022 Apr 20. Results Phys. 2022. PMID: 35469343 Free PMC article.
• Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm. Villanustre F, Chala A, Dev R, Xu L, LexisNexis JS, Furht B, Khoshgoftaar T. Villanustre F, et al. J Big Data. 2021;8(1):33. doi: 10.1186/s40537-021-00423-z. Epub 2021 Feb 15. J Big Data. 2021. PMID: 33614394 Free PMC article.
• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, Moraleda C, Rogers L, Daniels K, Green P. Crider K, et al. Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217. Cochrane Database Syst Rev. 2022. PMID: 36321557 Free PMC article.
• Tuberculosis. Bloom BR, Atun R, Cohen T, Dye C, Fraser H, Gomez GB, Knight G, Murray M, Nardell E, Rubin E, Salomon J, Vassall A, Volchenkov G, White R, Wilson D, Yadav P. Bloom BR, et al. In: Holmes KK, Bertozzi S, Bloom BR, Jha P, editors. Major Infectious Diseases. 3rd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017 Nov 3. Chapter 11. In: Holmes KK, Bertozzi S, Bloom BR, Jha P, editors. Major Infectious Diseases. 3rd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017 Nov 3. Chapter 11. PMID: 30212088 Free Books & Documents. Review.
• Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection. Dinnes J, Sharma P, Berhane S, van Wyk SS, Nyaaba N, Domen J, Taylor M, Cunningham J, Davenport C, Dittrich S, Emperador D, Hooft L, Leeflang MM, McInnes MD, Spijker R, Verbakel JY, Takwoingi Y, Taylor-Phillips S, Van den Bruel A, Deeks JJ; Cochrane COVID-19 Diagnostic Test Accuracy Group. Dinnes J, et al. Cochrane Database Syst Rev. 2022 Jul 22;7(7):CD013705. doi: 10.1002/14651858.CD013705.pub3. Cochrane Database Syst Rev. 2022. PMID: 35866452 Free PMC article. Review.
• COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves. Benavides EM, Ordobás Gavín M, Mallaina García R, de Miguel García S, Ortíz Pinto M, Doménech Gimenez R, Gandarillas Grande A. Benavides EM, et al. PLoS One. 2022 Dec 22;17(12):e0279080. doi: 10.1371/journal.pone.0279080. eCollection 2022. PLoS One. 2022. PMID: 36548226 Free PMC article.
• Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review. Afzal A, Saleel CA, Bhattacharyya S, Satish N, Samuel OD, Badruddin IA. Afzal A, et al. Arch Comput Methods Eng. 2022;29(2):1311-1337. doi: 10.1007/s11831-021-09634-2. Epub 2021 Aug 11. Arch Comput Methods Eng. 2022. PMID: 34393475 Free PMC article. Review.
• A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19. Dashtbali M, Mirzaie M. Dashtbali M, et al. Sci Rep. 2021 Apr 14;11(1):8191. doi: 10.1038/s41598-021-86873-0. Sci Rep. 2021. PMID: 33854079 Free PMC article.
• Impact of control interventions on COVID-19 population dynamics in Malaysia: a mathematical study. Abidemi A, Zainuddin ZM, Aziz NAB. Abidemi A, et al. Eur Phys J Plus. 2021;136(2):237. doi: 10.1140/epjp/s13360-021-01205-5. Epub 2021 Feb 19. Eur Phys J Plus. 2021. PMID: 33643757 Free PMC article.
• Kahn J.S., McIntosh K. History and recent advances in coronavirus discovery. Pediatr Infect Dis J. 2005;24(11 Suppl):S223–S227. doi: 10.1097/01.inf.0000188166.17324.60. discussion S226PMID 16378050. - DOI - PubMed
• Geller C., Varbanov M., Duval R.E. Human coronaviruses: insights into environmental resistance and its influence on the development of new antiseptic strategies. Viruses. November 2012;4(11) doi: 10.3390/v4113044. PMC 3509683. PMID 23202515. - DOI - PMC - PubMed
• Zhu Na, Zhang D., Wang W., Li X., Yang Bo, Song J., Zhao X., Huang B., Shi W., Lu R., Niu P. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–733. doi: 10.1056/NEJMoa2001017. ISSN 0028-4793. PMC 7092803. PMID 31978945. - DOI - PMC - PubMed
• Li Q., Guan X., Wu P., Wang X., Zhou L., Tong Y. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020 doi: 10.1056/NEJMoa2001316. - DOI - PMC - PubMed
• Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 doi: 10.1016/S0140-6736(20)30183-5. - DOI - PMC - PubMed

Related information

Linkout - more resources, full text sources.

• Elsevier Science
• Europe PubMed Central
• PubMed Central

Research Materials

• NCI CPTC Antibody Characterization Program
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Credit cards
• View all credit cards
• Banking guide
• Loans guide
• Insurance guide
• Personal finance
• View all personal finance
• Small business
• Small business guide
• View all taxes

You’re our first priority. Every time.

We believe everyone should be able to make financial decisions with confidence. And while our site doesn’t feature every company or financial product available on the market, we’re proud that the guidance we offer, the information we provide and the tools we create are objective, independent, straightforward — and free.

So how do we make money? Our partners compensate us. This may influence which products we review and write about (and where those products appear on the site), but it in no way affects our recommendations or advice, which are grounded in thousands of hours of research. Our partners cannot pay us to guarantee favorable reviews of their products or services. Here is a list of our partners .

Buying a House in 2024: What’s Changed?

Some or all of the mortgage lenders featured on our site are advertising partners of NerdWallet, but this does not influence our evaluations, lender star ratings or the order in which lenders are listed on the page. Our opinions are our own. Here is a list of our partners .

At the risk of jinxing it, things are looking up for home buyers.

The average rate on a 30-year fixed rate mortgage has dropped for three consecutive months (and counting). Competition has calmed down a bit — and inflation has, too. And while we’re still technically in a sellers’ market, the inventory of homes for sale in June reached its highest level in more than four years.

Hoping to buy in 2024? If you’re well prepared with a budget and a mortgage preapproval, you might not even need to knock on wood. Let’s look at the good news, the challenges and the wild cards that remain for home buyers this year.

Good news: Mortgage rates drop to a one-year low

Finally, some relief: In the week ending Aug. 15, 30-year mortgage rates dropped to an average 6.28%, their lowest weekly average since February 2023. That’s welcome news for shoppers who have felt burned by high rates — or maybe even put their house hunt on ice until the cost of borrowing cooled down.

Over the past two years, buyers have been at the mercy of mortgage rates’ meteoric rise, holding on as the average 30-year fixed rate climbed from 3% to nearly 7% in 2022. In October 2023, rates topped 8% for the first time since 2000 — a surprise even many top economists didn’t predict. Higher interest rates make it more expensive to get a mortgage.

To put that in perspective: Let’s say you can afford $1,800 per month in principal and interest. At a 7% interest rate, you could afford to borrow $270,600. But at a 6% interest rate, you could afford to borrow $300,200 — nearly $30,000 more — for the same amount per month. When interest rates go down, home shoppers’ purchasing power goes up.

For now, economic signals suggest more positive news for buyers in the latter half of 2024. Dan Moralez, regional vice president at Dart Bank in Holland, Michigan, points to a cooling economy and a potential cut to the federal funds rate. “All of that stuff really lends itself to mortgage rates getting better and the cost to borrow getting cheaper, which is really good for those people who have maybe sat on the sidelines hoping to see rates get better,” Moralez says.

More good news: It’s nearly certain the Federal Reserve will cut the federal funds rate by at least 25 basis points at its next meeting Sept. 17-18, according to CME Group’s FedWatch tool. (A basis point is one one-hundredth of one percent.) While the Fed doesn’t set mortgage rates directly, the federal funds rate influences the cost of long-term loans, including mortgages.

Your strategy: If you’re ready to buy, jump in now

A potential Fed rate cut is welcome news, but in the meantime, it’s not a reason to put off your search. Changes take time to trickle down, so avoid the self-induced pressure of timing the market perfectly. Instead, focus on shopping within your budget right now.

Also: When rates go down, competition goes up — another reason there’s no time like the present to start house hunting.

Whichever way rates move in the remainder of 2024, you’ll save money if you shop around. Aim to get an estimate from at least three mortgage lenders. The Consumer Financial Protection Bureau estimates borrowers can save $100 per month (or more) this way. And look at the annual percentage rate, or APR, to understand the total cost of the loan, which includes fees and other charges.

One final tip about rates: Do your research before picking a mortgage lender with the flashiest discount. This year, some lenders have been advertising “buy now, refinance later” offers. Others are offering temporary buydowns, where the buyer’s effective monthly payment is reduced for a year (or a few). Each option could potentially save money, but Moralez says it could also be “smoke and mirrors” if the deal is offset by higher fees.

“It’s one of those things where I tell folks, ‘There’s no free lunch, OK?’” he says. “You know, somebody is paying for it somewhere.”

Good news: More inventory, less intense competition

Recently, the supply of homes for sale could be summed up in two words: Slim pickings.

But in June, shoppers got some good news: The number of existing homes for sale reached a four-year high, according to the National Association of Realtors (NAR). Nationwide, there was a 4.1-month supply of homes for sale, meaning it would take just over four months at the current pace for all properties to sell. The U.S. market hasn’t seen that much housing inventory since May 2020, when the supply was 4.5 months.

Demand still outpaces supply, but with more homes to choose from, buyers are less likely to encounter intense bidding wars reminiscent of the pandemic years. Houses for sale are getting fewer offers compared to last year, according to the NAR’s June 2024 Realtors Confidence Index, a survey of its members. In June, a home listed for sale received an average 2.9 offers, compared to 3.5 offers in June 2023.

Another sign of cooling competition: Houses are staying on the market longer. In June, 65% of homes sold in less than a month, compared to 75% at the same time last year. The median time on the market in June was 22 days, a full four days longer than June 2023, when the median time on the market was 18 days.

With pending home sales also on the rise in June, NAR Chief Economist Lawrence Yun says he expects to see even more houses getting listed ahead of typical seasonal declines in winter. "The rise in housing inventory is beginning to lead to more contract signings," Yun said in a news release. "Multiple offers are less intense, and buyers are in a more favorable position."

Your strategy: Cast a wide net

While an improvement from recent years, a 4.1-month supply of homes for sale is still technically a seller’s market. A balanced market has about a six-month supply of homes for sale; a buyer’s market has more than six months’ worth.

You can’t control who puts their house on the market, so in the meantime, focus on the options available now. Let go of the fantasy of finding the perfect home when a “good enough” home can get your foot in the door sooner. That’s especially true for first-time home buyers who are eager to build equity.

“Last year, we certainly didn’t have enough houses — and we still don’t,” says Ellie Kowalchik, a real estate agent who leads the Move2Team with Keller Williams Pinnacle Group in Cincinnati, Ohio. “Don’t wait until the spring to start looking.”

For now, maybe you expand your search to include condos or townhouses. Maybe you settle for fewer bathrooms or a dated interior. Keep your chin up — even if you have to tolerate less square footage or weird linoleum floors for a while, you’ll have equity to remodel or sell in a few years.

Still challenging: Home prices climb to record highs

While some aspects of homebuying have gotten easier as 2024 rolls on, one challenge remains: home prices. The sales price of existing homes has risen for 12 straight months, according to the NAR. In June, the national median sales price hit a record high of $426,900.

As more inventory hits the market, though, the degree of home price growth has slowed somewhat over the summer, according to an August 2024 report from ICE Mortgage Technology. Still, if you compare the cost of buying a house to the median household income, July 2024 was one of the least affordable months to buy a home in more than three decades. Why? Home prices are growing faster than wages, and on top of that, high mortgage rates increase the cost of borrowing.

Until supply catches up to demand, prices are unlikely to fall. Realtor.com estimates prices will fall less than 2% by the end of 2024. No one can predict exactly what the market will do, but if you’re an optimist, there’s reason to be hopeful that prices are reaching a plateau.

“Even as the median home price reached a new record high, further large accelerations are unlikely,” Yun said in a press release. “Supply and demand dynamics are nearing a balanced market condition.”

That’s another reason to jump in now: A big drop in prices could trigger more competition.

Your strategy: Make a budget and stick to it

If you’re Zillow-stalking houses you can’t afford, stop. Instead, channel that energy toward your plan to shop for a house in real life — starting with setting a realistic budget.

First, talk to a financial advisor or use an online calculator to see how much house you can afford . Understand how mortgage lenders will determine your eligibility, including analyzing your credit score, cash savings and monthly debt payments.

Next, find a buyer’s agent who knows how far your budget can go in your local market. An experienced agent can advocate for you and help you snag a good deal.

Wild card: Changes to real estate commissions

One of the year’s biggest shakeups has been a major legal settlement with the NAR, which changes the way your buyer’s agent gets paid. While the NAR admitted to no wrongdoing, it will pay $418 million to settle more than a dozen antitrust lawsuits accusing the organization of enforcing rules that inflated real estate commissions. These changes take effect Aug. 17.

Previously, home sellers generally set the agents’ commission — typically 5% to 6% of the home sale price that was then split between the buyer’s and seller’s agent. Now, a new system is in place: You'll have to sign a contract with your buyer’s agent, which spells out the terms of how they get paid.

For now, many real estate brokerages will likely stick with the familiar commission structure of a percentage of the sales price. But the settlement opens the door for new ways for agents to get paid, such as a flat fee or an hourly rate. Time will tell what becomes the new standard.

Your strategy: Brush up on your negotiating skills

When hiring a buyer’s agent , be polite but firm when negotiating. If the commission is more than you want to spend, ask if the agent would be willing to lower it. Point out any fees you don’t understand. And if you still aren’t comfortable with the terms, it’s OK to shop around or walk away.

While the new rules are more complex, they also give you, the buyer, more leverage in negotiating for your best interests. Buying a home is a big journey, and when you sign that contract with a buyer's agent, you should feel supported and empowered about the business relationship that lies ahead.

The bottom line: Set realistic expectations

Things are looking better compared to the beginning of this year, but if you haven’t found a house yet, it’s fair to feel bummed out about high costs and complexity.

The solution: Think long-term. Holding out for lower rates or “perfect” buying conditions likely means you’ll face steeper prices and more competition. So if you’re determined to buy, find a place that suits your needs and budget as-is. Expecting perfection often means setting yourself up for disappointment.

“Sometimes I have clients that think they're going to hit a home run the very first house they buy,” Moralez says. “And a lot of times I tell clients, well, sometimes it's OK to be happy just getting on base.”

On a similar note...

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread

1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; moc.361@39990183131 (Y.F.); moc.361@4131nawxav (Z.X.)

2 Beijing Institute of Smart City, Beijing University of Technology, Beijing 100124, China

The Corona Virus Disease 2019 (COVID-19) is spreading all over the world. Quantitative analysis of the effects of various factors on the spread of the epidemic will help people better understand the transmission characteristics of SARS-CoV-2, thus providing a theoretical basis for governments to develop epidemic prevention and control strategies. This article uses public data sets from The Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE), Air Quality Open Data Platform, China Meteorological Data Network, and WorldPop website to construct experimental data. The epidemic situation is predicted by Dual-link BiGRU Network, and the relationship between epidemic spread and various feature factors is quantitatively analyzed by the Gauss-Newton iteration Method. The study found that population density has the greatest positive correlation to the spread of the epidemic among the selected feature factors, followed by the number of landing flights. The number of newly diagnosed daily will increase by 1.08% for every 1% of the population density, the number of newly diagnosed daily will increase by 0.98% for every 1% of the number of landing flights. The results of this study show that the control of social distance and population movement has a high priority in epidemic prevention and control strategies, and it can play a very important role in controlling the spread of the epidemic.

1. Introduction

Since December 2019, The Corona Virus Disease 2019 (COVID-19) caused by the SARS-CoV-2, has spread rapidly around the world. On 11 March 2020, the WHO announced that COVID-19 has become a major issue in the world [ 1 , 2 , 3 , 4 ]. The spread of COVID-19 has had a serious impact on the medical and economic aspects of countries around the world [ 5 ]. Due to the complexity of the spread of COVID-19, existing models cannot accurately estimate the direction of the spread of the epidemic [ 6 ]. Therefore, we need to build a quantitative analysis model to deeply explore the spread and influencing factors of COVID-19 on a global scale. In the current research, the data-driven deep learning model has an outstanding performance in the task of modeling time series [ 7 ].

The symptoms of COVID-19 are fever, cough, shortness of breath, loss of consciousness and fatigue. Other symptoms include dyspnea and chest pain [ 8 ]. In order to prevent the spread of the epidemic, countries have adopted many measures, such as reducing gathering activities, controlling the movement of people, advocating the use of masks, and regular disinfection in public areas [ 9 ]. As of 31 December 2021, there have been more than 287 million confirmed cases of COVID-19 worldwide, and at least 5 million people have lost their lives [ 10 ]. In order to further grasp the factors affecting the spread of SARS-CoV-2, better support the decision-making of epidemic prevention and control, timely made targeted countermeasures, and control the further spread of the epidemic, it is very urgent to quantitatively analyze the relationship between various factors and the spread of SARS-CoV-2.

The remainder of this paper is arranged as follows. Section 2 comprehensively introduces the current research on COVID-19 and the transmission characteristics of the SARS-CoV-2. Section 3 introduces the data sources and presents our research methodology. Section 4 describes the experimental results and provides an analytical discussion, and Section 5 summarizes the conclusions of this study and proposes further research directions.

2. Related Research Work

2.1. research on covid-19 epidemic.

Since COVID-19 outbreak in December 2019, research on COVID-19 has attracted the attention of data scientists from all over the world. Duccio et al. [ 11 ] predicted that the maximum number of infections in Italy was about 26,000 and the death toll was about 18,000 through analysis of the spread of the epidemic in China and France. Ricardo et al. [ 12 ] proposed a regression of compressed space Gaussian processes based on chaotic dynamics system to predict the number of people infected with COVID-19 in the United States, and concluded that the number of infected people in the United States would reach more than one million on 14 June 2020. Rohit et al. [ 13 ] proposed Genetic Evolutionary Programming (GEP) to analyze and predict the amount of COVID-19 cases in India. They proposed a GEP model based on the use of a simple function, which was highly effective for the time series prediction of COVID-19 cases in India. Putra et al. [ 14 ] used Particle Swarm Optimization (PSO) to estimate the parameters in the Susceptible Infectives Recovered Model (SIR), and concluded that the parameter results of the PSO algorithm were more accurate and had lower errors than the traditional method. Mbuvha et al. [ 15 ] estimated the parameters of the SIR with data from Lombardy, Italy and Hubei, China, and used the SIR model to predict the number of COVID-19 cases in South Africa, and concluded that COVID-19 was still in the early stage in South Africa.

So far, some scholars have done excellent research, but if it is necessary to further study the transmission characteristics of the SARS-CoV-2, it is impossible to predict the number of patients only. It is necessary to collect data related to the spread of SARS-CoV-2, and to analyze the characteristics of SARS-CoV-2 to understand what factors are related to the spread of SARS-CoV-2 and the quantitative relationship between them, so as to support the more precise adoption of effective prevention, control and disposal measures.

2.2. Research on the Transmission Characteristics of the SARS-CoV-2 Virus

When COVID-19 became a global hot topic, people put forward many speculations that could affect the transmission characteristics of the SARS-CoV-2, such as temperature [ 16 , 17 , 18 ], humidity [ 19 , 20 ], population density [ 21 , 22 ], age [ 23 , 24 ], and so on. In this regard, scholars have also conducted a lot of research, which has a non-negligible inspiration for us to reveal the transmission characteristics of the SARS-CoV-2. Lin et al. [ 25 ] studied the relationship between climate and the spread of COVID-19 on a global scale, and concluded that the spread of COVID-19 was highly correlated with temperature and relative humidity. Roengrudee et al. [ 26 ] studied the relationship between smoking and the spread of COVID-19, and concluded that there was a significant correlation between the number of smokers and the spread of COVID-19. Kass et al. [ 27 ] analyzed the relationship between Body Mass Index (BMI) and age in the number of confirmed COVID-19 patients through a multiple linear regression model, and concluded that obesity may increase the infection rate of COVID-19. WU et al. [ 28 ] found that in the United States, areas with higher historical PM2.5 were positively correlated with higher COVID-19 mortality. Hamit et al. [ 29 ] found that population density was the main factor affecting the spread of the epidemic through research on the spread of the epidemic in Turkish cities.

The above-mentioned studies generally have the following problems: (1) The area covered by the data set is limited to local areas, and the propagation characteristics of SARS-CoV-2 cannot be analyzed from a global scale. (2) The conclusion is only a qualitative analysis, and it has not been able to quantify the effects of various factors on the impact of the spread of the SARS-CoV-2. In response to the above problems, this paper constructs a quantitative analysis model between COVID-19 and multiple factors. Firstly, we collect the required data on a global scale, and then build a Dual-link BiGRU prediction network to predict the number of new cases in each country every day, and quantitatively analyze the impact of different factors on the number of new cases per day of COVID-19. Compared with the above research, the model proposed in this paper is more helpful to analyze the development trend of the epidemic on a global scale, helps to grasp the characteristics of the SARS-CoV-2, and provides more clear theoretical support for the subsequent formulation of anti-epidemic policies by governments of various countries.

3. Data Sources

The data set in this paper is mainly divided into four parts including epidemic data, climate data, population and flight data, and air quality data.

• The source of the epidemic data is COVID-19 data set published by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The data set was collected from all over the world from 22 January 2020, in the early stage of the epidemic. The experimental data in this article include the collected epidemic data from 22 January 2020 to 31 December 2021. The feature data elements include the cumulative number of confirmed cases, the cumulative number of cured people, the cumulative number of deaths, and the number of new cases per day.
• The climate data comes from the daily recorded data of weather stations around the world collected by the China Meteorological Data Network ( http://data.cma.cn/ ). This experiment selects the climate data of various regions from 22 January 2020 to 31 December 2021. The feature data elements include daily maximum temperature, daily minimum temperature, wind speed, precipitation, dew point temperature, atmospheric pressure, wind gust, altitude, absolute humidity and relative humidity.
• The population and flight data come from the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. ( https://population.un.org/wpp/ ). This experiment selects population and flight data in various regions from 22 January 2020 to 31 December 2021. The feature data elements include total population, population density, the total number of flights, number of domestic flights, and international flights.
• The air quality data come from the open-source air quality website WAQI ( https://aqicn.org/data-platform/covid19/ ). This experiment selects air quality data in various regions from 22 January 2020 to 31 December 2021. The feature data elements include NO 2 , PM 10 , PM 2.5 , PM 1 , SO 2 , O 3 , CO content in the air, Air Quality Index(AQI), Suspended particle concentration(from NEPH), UV Index(UVI), Pollution(POL) and Wavelength Dominant(WD).

We collected 31-dimensional features of 81 countries to form a data set. Because we can get the data we need in these countries, we selected these 81 countries. In order to ensure that there was a sufficient amount of data to train the model, we selected the 9:1 segmentation ratio to divide the training set and test set, that is, the data from 22 January 2020 to 31 October 2021 was set as the training set and that from 1 November 2021 to 31 December 2021 as the test set.

4. Research Methods

The quantitative relationship model between COVID-19 spread and various characteristic factors proposed in this paper includes three steps: multi-source heterogeneous data preprocessing, constructing Dual-link BiGRU Network to prediction COVID-19 spread, and building a quantitative analysis model of multiple feature data relationships.

4.1. Multi-Source Heterogeneous Data Preprocessing

Because the data comes from a variety of public data sets, there are some problems among data sets, such as inaccurate data, missing data, inconsistent data format and etc. In the data preprocessing stage, this paper builds a dataset with the original data as the core. For inaccurate data, when the values of the same feature data in datasets from different sources are the same, we consider the data to be reasonable; otherwise, most of the data in datasets from different sources are selected as the final data. For missing data, the Cubic Spline Interpolation method is used to supplement the data. For inconsistent data format, feature level fusion method is adopted to extract the features of each source data set first, while the extracted feature information comes from the high-order representation of the original information, and then to aggregate and synthesize the multi-source data according to the feature information. The data with inconsistent scales are normalized by the linear normalization method to unify the data scale. This is also a commonly used data preprocessing method in the field of COVID-19 prediction. The information contained in the fused data is shown in Table 1 .

Feature display of fusion data set.

Tmax, Tmin, Wind_speed, Precipitation, DP_F, Pressure, Wind_gust, Altitude, Ab_humidity and Re_humidity represent daily maximum temperature, daily minimum temperature, daily average wind speed, daily rainfall, daily dew point temperature, atmospheric pressure, wind gust, altitude, absolute humidity and relative humidity. Pop, Density represent total population, population density. NO 2 , PM 10 , PM 2.5 , PM 1 , SO 2 , O 3 , CO and AQI, NEPH, UVI, POL, WD represent NO 2 , PM 10 , PM 2.5 , PM 1 , SO 2 , O 3 , CO content in the air, Air Quality Index(AQI), Suspended particle concentration(from NEPH), UV Index(UVI), Pollution(POL) and Wavelength Dominant(WD). Flight_total, Flight_domestic, and Flight_international represent the total number of flights, the number of domestic flights, and the number of international flights respectively.

4.2. Dual-Link BiGRU Network to Predict the Spread of COVID-19

In this paper, we construct Dual-link BiGRU Network to predict the spread of COVID-19. The task of Dual-link BiGRU is to regress and predict the number of new cases per day with input data. Dual-link BiGRU conducts parameter training through the relationship between daily different factors in the training set and the number of new cases. It inputs the values of the daily factors in the test set, and outputs the regression estimation of the number of new cases on that day. The network structure diagram of Dual-link BiGRU is shown in Figure 1 .

The network structure diagram of Dual-link BiGRU.

Dual-link BiGRU consists of a dual-link feature network and a fully connected network. In the feature network, Link 1 is composed of one-dimensional convolutional network, BiGRU network, and one-dimensional inverse convolutional network. Link 2 is composed of one-dimensional convolutional network, fully connected network, and one-dimensional inverse convolutional network. Link 1 is mainly responsible for learning the timing information in the data of multiple factors. The one-dimensional convolutional network in Link 2 provides a larger receptive field for the network with a larger size of convolution kernel to learn different feature information from Link 1. In this experiment, in order to obtain a larger receptive field and better features, we select the kernel size of 16. After the dual-link feature network is a fully connected network. The fully connected network’s main function is to change the output dimension of the entire Dual-link BiGRU network to the desired output dimension.

According to the prediction performance of the test set, the parameter settings of the prediction network are shown in Table 2 . The optimizer used for model training is Adam, the loss function is Mean Squared Error Loss Function (MSELoss), and the number of iterations is set to 500. In this paper, we selects BiLSTM [ 30 ], BiGRU [ 31 ], and CNN [ 32 ] for comparison at the same dataset which comes from Table 1 . BiLSTM, BiGRU, and CNN are connected by their respective models and fully connected layers. The hidden layer size and number of layers of BiLSTM and BiGRU are consistent with Dual-link BiGRU, and the parameter setting of CNN is consistent with 1-D Conv1 in Dual-link BiGRU.

Prediction network parameter settings.

4.3. The Quantitative Analysis Model of Multi Characteristic Data Relationships

In this paper, we sets the tolerance of the prediction error rate β ∈ [0, 1]. The model with a prediction error rate lower than β is called an effective model, otherwise it is called an invalid model. It is assumed that only effective models can participate in quantitative analysis. Therefore, the larger of β means the more effective models, and the quantitative analysis results have better generalization ability, but it also means that the results have larger errors; the smaller of β means the less effective the models and the poorer generalization ability of the quantitative analysis results, while the results have smaller errors within a limited range. This paper needs to have a small result errors on the basis of ensuring a certain generalization ability, so β = 0.2 is set in the experiment of this paper.

In this paper, the Gauss-Newton iterative method is used for quantitative analysis. The Gauss-Newton iterative method uses Taylor series expansion to approximately replace the nonlinear regression model. Through multiple iterations, the regression coefficient is modified many times, so that the regression coefficient continuously approaches the best regression coefficient of the nonlinear regression model, and finally the Residual Sum of Square of the original model is minimized.

According to the selected observation variable data, a multiple nonlinear regression model as in Equation ( 1 ) can be constructed.

where y is the dependent variable, which represents the number of newly diagnosed people every day in this experiment; X is the set of independent variables, which represents the data of each characteristic factor in this experiment; β is an unknown parameter; ϵ is an error term, and it is an unobservable random variable with a mean of zero and a variance of σ 2 > 0 . The above model can be used to predict the number of the newly diagnosed daily and determine the nonlinear quantitative relationship between each independent variable and the dependent variable. The Gauss-Newton iteration method estimates the to-be-regressed parameter β of the nonlinear regression model through continuous iteration.

The realization process of the quantitative analysis model includes the following steps:

• Construct multiple regression models and train through data;
• The prediction ability of the model is evaluated by modifying the determination coefficient;
• The quantitative relationship between multiple factors and the number of new cases per day was determined by a multiple regression model;
• Given different initial values for different factors x 0 ;
• For the k t h iteration, calculate the Jacobian matrix J , Hessian matrix H , B , and calculate the increment △ x k ;
• If △ x k is small enough, stop the iteration, otherwise, update x ( k + 1 ) = x k + △ x k ;
• Repeat steps (5) (6) until the maximum number of iterations is reached, or the termination condition of (6) is met;
• Complete the estimation of the unknown parameter β , and determine the quantitative relationship between different elements and the number of new cases per day;
• Complete for β to determine the quantitative relationship between different elements and the number of new cases per day.

5. Experimental Results and Discussion

5.1. dual-link bigru.

In this paper, the evaluation index is selected as the error rate ρ , and the error rate calculation formula is shown in Equation ( 2 ):

where y ^ i represents the model output, y i represents the label of the number of new cases per day, and m represents the total number of samples in the test set. This indicator can measure the gap between the model output and the label of the entire test set sample.

In this paper, we selects BiLSTM [ 30 ], BiGRU [ 31 ], and CNN [ 32 ] for comparison at the same dataset which comes from Table 1 . BiLSTM, BiGRU, and CNN are connected by their respective models and fully connected layers. The hidden layer size and number of layers of BiLSTM and BiGRU are consistent with Dual-link BiGRU in Table 2 , and the parameter setting of CNN is consistent with 1-D Conv1 in Dual-link BiGRU in Table 2 . Sets the prediction error tolerance β = 0.2, and uses the model error rate as the evaluation index. In the data of 81 countries, the model with an error rate lower than β is regarded as an effective model, and the difference in the number of effective models among different models is compared in the test dataset. The comparison experiment results are shown in Table 3 .

Comparison of model results.

Table 3 shows that (1) Dual-link BiGRU has a larger effective model ratio in the prediction network; (2) Compared with BiGRU, BiLSTM, and CNN, Dual-link BiGRU performs better in low error rate. Therefore, it is believed that the Dual-link BiGRU has better performance and generalization ability in predicting the daily number of new epidemics in various countries. Therefore, this paper selects the Dual-link BiGRU as the prediction network. Figure 2 shows the difference between the daily number of new cases predicted of the Dual-link BiGRU and the label value. Because showing the forecast results for all countries would make the paper extraordinarily long, in this paper, we select 6 countries with better results for display, including Canada, China, India, Indonesia, Russia, and United Kingdom.

Display of Dual-link BiGRU prediction results.

It can be seen from Figure 2 that in the selected 6 countries, the red solid line is the label of the number of new cases per day, and the green dashed line is the predicted value by the Dual-link BiGRU network. The two curves have a high degree of overlap. Therefore, the prediction network constructed in the experiment has a good fit with the real data. The trained prediction network can better predict the daily new cases and has a strong generalization ability. For different countries, the model can learn more appropriate parameters to predict the number of the daily new cases.

5.2. Quantitative Analysis Results of Multi-Characteristic Data Relationships

In this paper, we uses the method of Lin [ 25 ] and others to build a multiple regression model for the selected 44 effective national models and train them. Through the multiple regression model, the quantitative relationship between multiple factors and the number of new cases per day is determined, and the prediction ability of the model is evaluated by determining the Adjusted R Square (R). The larger R is, the stronger the prediction ability of the model is. If R is greater than 0.6, the model has strong epidemic prediction ability. Then, the initial value of the Gauss-Newton iterative method is selected through the model parameters. The quantitative relationship between multiple factors and the number of new cases per day is shown in Table 4 , and the initial values are shown in Table 5 .

Regression equation parameter.

Example of initial value of each characteristic coefficient.

In this paper, we uses the trained Dual-link BiGRU model of various countries to generate simulation data for quantitative analysis. The data generation method is as follows:

• Goal: To generate data for analyzing the quantitative relationship between x 1 and y , where x 1 is the maximum temperature per day and y is the number of new cases per day.
• To control other factors unchanged, adjust x 1 , and generate the predicted value of y .
• The simulation data is used as input, and training is performed with the Gauss-Newton method to obtain the coefficient between x 1 and y , so as to determine the quantitative relationship between them.

According to the above method, the coefficient equations between the number of new cases per day in each country and the characteristic factors in Table 1 are obtained respectively, and the quantitative relationship between the number of new cases per day and the characteristic factors in each country is determined. Then take the average of the quantitative relationship coefficients of the same feature in all countries, and finally get the quantitative relationship between each feature that is applicable in the selected country and the number of new cases per day with strong generalization performance, as shown in Table 6 .

Quantitative relationship between characteristic factors and daily number of new cases.

The influence >0, indicating that the factor has a positive correlation with the increase in the number of new cases per day. The influence <0, indicating that the factor has a negative correlation with the increase in the number of new cases per day.

As shown in Table 6 , among the selected features, the population density per unit land area has the largest positive correlation with the number of new cases per day, followed by the number of landing flights. The population density per square kilometer increases by 1%, and the number of new cases per day in the corresponding area increases by about 1.076%. For every 1% increase in the number of landing flights, the number of new cases per day in the corresponding area increases by about 0.98%. Among the selected features, the daily maximum temperature, daily minimum temperature and dew point temperature have negative correlations to the number of new cases per day. Within the range of 0–50 °C, each increase of 1 °C can reduce the number of new cases per day by 0.021%, 0.028% and 0.015% respectively.

Based on the above analysis, the following further inferences can be drawn:

• Population factors and flight factors has an obvious positive correlation impact on the spread of COVID-19. From the data of the selected 44 countries, it can be seen that population factors and flight factors have a greater impact on the spread of COVID-19. Every 1% increase in population factors will increase the spread of the epidemic by 1.044%. Every 1% increase in the number of arrival flights will increase the spread of the epidemic by 0.98%. Therefore it can be seen that population factors and flight factors have a more obvious impact on the increase in the spread of the epidemic. From the perspective of formulating epidemic prevention and control policies, controlling social distancing and population movement will have a more obvious positive correlation impact on epidemic prevention and control.
• The increase in temperature and relative humidity has a negative correlation impact on the spread of COVID-19.Among the climatic factors, the increase of temperature and humidity has a negative correlation impact on the spread of COVID-19. In this paper, the temperature range of 0–50 °C and the relative humidity range of 1–100% are selected for the experiment. It is obtained that within this range, temperature and relative humidity has a negative correlation impact on the spread of COVID-19, but the impact is not obvious. Since the correlation between population density and the speed of the epidemic is far greater than the correlation between temperature and the speed of the epidemic, it is speculated that in areas with higher temperatures and higher population densities, such as India and other countries, the speed of the epidemic still has a relatively rapid possibility.
• A larger AQI has a positive correlation impact on the spread of COVID-19.AQI represents the degree of air cleanliness or pollution and its impact on health. The higher the AQI, the more serious the air pollution in the region. This experiment shows that in the range of AQI value 100–200, the epidemic transmission speed of COVID-19 will increase by 0.013% every time AQI increases by 1. Some researchers have shown that SARS-CoV-2 can spread through aerosols [ 33 , 34 , 35 ]. Therefore, a higher AQI means a higher aerosol content in the air, which is not good for air circulation. Such an environment may promote the spread of COVID-19.

6. Discussion

Since the discovery of COVID-19 in 2019, countries have successively formulated epidemic prevention and control policies that suit their own national conditions [ 36 ]. According to the current development status of the world epidemic, a long-term coexistence with the virus has been formed, that is, even though the vaccine has been developed, it will take a long time to completely eliminate COVID-19 [ 37 , 38 ]. This paper carries out quantitative analysis and research on COVID-19 transmission by various factors all over the world and comes to the conclusion that the increase of population density, population flow, and flight times has a positively correlated impact on the epidemic transmission, and the increase of temperature, relative humidity, and dew point temperature has a negative correlation impact on the epidemic transmission. It can be concluded that the positive correlation effect of population density on the epidemic spread is much greater than the negative correlation effect of climate factors on the epidemic spread.

Therefore, according to the regional characteristics and national conditions, governments should formulate epidemic prevention and control policies to control population density and population flow in the climate environment with high local temperature and relative humidity, maximize the effect of epidemic prevention and control, and curb the spread of the epidemic from the aspects of transmission route and virus characteristics.

International organizations need to establish high, medium and low-risk epidemic spread levels globally. The faster the epidemic spread, the higher the epidemic spread level, and the more stringent prevention and control policies need to be adopted. For cities where the epidemic has spread, it is necessary to keep wearing masks, maintain proper social distancing, and reduce public recreational activities. For cities with large population density and serious epidemic spread, it is recommended to strictly control population flow, tighten restrictive measures for international flights, and take “city closure” measures when necessary, and other cities need to take more stringent entry epidemic prevention measures for personnel from high-risk countries and regions. For cities with slow epidemic spread, it is suggested to control the population flow within a certain range, allow international flights under the condition of good epidemic prevention measures, strictly control the flow of people from high-risk countries and regions, and be vigilant against the epidemic spread caused by climate change.

7. Conclusions and Future Work

In this paper, we fuses multi-source heterogeneous data, and makes predictions for the current COVID-19 epidemic based on the fusion data set, and quantitatively analyzes the model to obtain the quantitative relationship between various factors and the spread of the epidemic. The contributions of this paper are as follows:

• Compared with the CNN, LSTM, and GRU networks, the prediction accuracy of the Dual-link BiGRU network is improved by 35.03%, 31.41%, and 27.36%, respectively;
• Compared with the CNN, LSTM, and GRU networks, the generalization ability of the Dual-link BiGRU network is improved by 25.00%, 27.50%, and 28.75%, respectively.
• The increase in population factors and flight factors has an obvious positively correlated impact on the spread of COVID-19.
• The increase in AQI will has a minor positively correlated impact on the spread of COVID-19.
• The increase in temperature and relative humidity has a negative correlation impact on the spread of COVID-19.

Accordingly, this paper makes the following recommendations for global epidemic prevention and control:

• Countries should take appropriate or even stricter prevention and control measures according to their national conditions, such as demographic factors, climate factors, air quality factors, and the number of flights, to minimize the risk of outbreaks.
• Demographic factors have a strong positive relationship with the spread of COVID-19 epidemic. Governments can control the spread of the epidemic by strictly controlling the movement of people both within and outside the country.
• Since the impact of population and flight factors on the spread of the epidemic is much greater than that of climate factors, governments of various countries should not expect the epidemic to disappear after the temperature rises, and should actively control population movement.

This paper has completed the multi-factor quantitative analysis model affecting the spread of COVID-19. Due to the different detection coverage of COVID-19 in various countries, the number of confirmed cases is inevitably underestimated, and this paper does not evaluate the impact of changes in policies and local prevention and control strategies on the spread of COVID-19. Therefore, more detailed exploration is needed on these issues in the next step.

Author Contributions

Y.F. collected literature and wrote the manuscript; S.L. reviewed and edited the manuscript; Z.X. Put forward suggestions for revision of the article. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.