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• Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

• Israel Júnior Borges do Nascimento 1 , 2 ,
• Dónal P. O’Mathúna 3 , 4 ,
• Thilo Caspar von Groote 5 ,
• Hebatullah Mohamed Abdulazeem 6 ,
• Ishanka Weerasekara 7 , 8 ,
• Ana Marusic 9 ,
• Livia Puljak   ORCID: orcid.org/0000-0002-8467-6061 10 ,
• Vinicius Tassoni Civile 11 ,
• Irena Zakarija-Grkovic 9 ,
• Tina Poklepovic Pericic 9 ,
• Alvaro Nagib Atallah 11 ,
• Santino Filoso 12 ,
• Nicola Luigi Bragazzi 13 &
• Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume  21 , Article number:  525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

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Dónal P. O’Mathúna

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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A literature review of the economics of COVID‐19

Abel brodeur.

1 Department of Economics, University of Ottawa, Ottawa Ontario, Canada

Suraiya Bhuiyan

Associated data.

Table B: COVID‐19 ‐ Timeline

Figure A: Cumulative COVID‐19 Cases and Deaths – Global Pandemic (as on 30 November 2020)

Table C: Cumulative Cases: Top 10 Countries (as of 30 November 2020)

The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID‐19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID‐19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID‐19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.

1. INTRODUCTION

The World was gripped by a pandemic over the first half of 2020, of which the second wave emerged in the Fall. It was identified as a new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS‐CoV‐2), and later renamed as Coronavirus Disease‐19 or COVID‐19 (Qiu et al., 2020 ). While COVID‐19 originated in the city of Wuhan in the Hubei province of China, it has spread rapidly across the World, resulting in a human tragedy and in tremendous economic damage. By the end of November 2020, there had been close to 63 million reported cases of COVID‐19 globally and over 1.4 million deaths.

Pandemics are anything but new, and they have had severe, adverse economic impacts in the past; COVID‐19 is not expected to be any different (see the Online Appendix for a brief history of past pandemics and their socioeconomic consequences). Given the rapid spread of COVID‐19, countries across the World have adopted several public health measures intended to prevent its spread, including social distancing (Fong et al., 2020 ). According to Mandavilli ( 2020 ), this strategy saved thousands of lives, both during other pandemics, such as the Spanish flu of 1918, and more recently a flu outbreak that occurred in Mexico City in 2009. As part of social distancing measures, businesses, schools, community centers, and nongovernmental organization (NGOs) were required to close down, mass gatherings have been prohibited, and lockdown measures have been imposed in many countries, allowing travel only for essential needs. 1 The goal of these measures is to facilitate a “flattening the curve,” that is, a reduction in the number of new daily cases of COVID‐19 in order to halt their exponential growth and, hence, reduce pressure on medical services (John Hopkins University, 2020 ).

The spread of COVID‐19 has resulted in a considerable slowdown in economic activities. According to an early forecast of The World Bank ( 2020 ), global GDP in 2020 relative to 2019 is forecasted to fall by 5.2%. Similarly, the OECD ( 2020 ) forecasts a fall in global GDP by 6 to 7.6%, depending on whether or not a second wave of COVID‐19 emerges. In its latest forecast, the International Monetary Fund ( 2020 ) projected a contraction of 4.4% in light of the stronger than expected recoveries in advanced economies which lifted lockdowns during May and June of 2020. This was mainly the result of the unprecedented fiscal, monetary, and regulatory responses in these countries that helped to maintain household disposable income, protect cash flows for firms, and support credit provisions.

The economic implications will be wide ranging and uncertain, with different effects expected on labor markets, production supply chains, financial markets, and GDP levels. The negative effects may vary by the stringency of the social distancing measures (e.g., lockdowns and related restrictions), their length of implementation, and the degree of compliance with them. In addition, the pandemic and the subsequent interventions may well lead to higher levels of mental health distress, increased economic inequality, and particularly harsh effects on certain socio‐demographic groups.

The goal of this piece is to survey the emerging and already vast literature on the economic consequences of COVID‐19, and to synthesize the insights contained in a growing number of studies. Figure  1 illustrates the number of National Bureau of Economic Research (NBER) working articles that have been released related to the pandemic between March and November of 2020. 2 By the end of November 2020, there had been 247 articles related to COVID‐19. Similarly, 204 discussion articles on the pandemic were released by the IZA Institute of Labor Economics (IZA) from March to November of 2020. 3

COVID‐19 publications in 2020 in the NBER working paper series. [Color figure can be viewed at wileyonlinelibrary.com ]

Source : Authors’ compilation drawn from the NBER website

This article will focus on five broad areas: (i) the measurement of the spread of COVID‐19 and social distancing activities, (ii) the effectiveness and compliance with social distancing regulations, (iii) the economic impacts of COVID‐19 and the mechanisms giving rise to them, (iv) the socioeconomic consequences of lockdowns, and (v) the policy measures and regulations that have been implemented in response to the pandemic. One topic that we do not cover explicitly is the interface between COVID‐19 and financial markets. This omission is due partly to space constraints, but also to the fact that the outcomes in financial markets that are related to COVID‐19 are extremely volatile, and therefore, any analysis contained in our survey would be ephemeral.

The rest of the article is structured as follows. Section  2 provides an outline of the measurement of COVID‐19 spread and of social distancing actions by documenting and describing the most popular data sources. Section  3 discusses the socioeconomic determinants and the effectiveness of social distancing activities. Section  4 focuses on the economic and financial impacts including modelling of the plausible behavioral mechanisms. Section  5 reviews the literature on the socioeconomic consequences of social distancing measures, focusing on the labor‐related, health‐related, gender‐related, discriminatory, and environmental aspects. Section  6 consists of a summary of the economic impact of the policy responses. Section  7 provides the conclusion.

2. MEASUREMENT OF COVID‐19 AND SOCIAL DISTANCING ACTIONS

2.1. measurement of covid‐19 spread.

Before reviewing the potential economic impact and socioeconomic consequences, it is important to contextualize the data related to COVID‐19, without which it would not be possible to assess the scope of the pandemic. Timely and reliable data inform us of how and where the disease is spreading, what impact the pandemic has on the lives of people around the World, and to what extent the counter measures that are taken are successful (Roser et al., 2020 ).

Four key indicators are: (i) the total number of tests carried out, (ii) the number of confirmed COVID‐19 cases, (iii) the number of confirmed COVID‐19 deaths, and (iv) the number of people who have recovered from COVID‐19. These numbers are provided by different local, regional, and national health agencies/ministries across countries. However, for research and educational purposes, the data are accumulated by the Center for Systems Science and Engineering at Johns Hopkins University. 4 The database provides the figures as well as visual maps of the distribution of cases across the World. They are reported at the provincial level for China, at the city level for the United States of America, Australia, and Canada, and at the country level for all other countries (Dong et al., 2020 ). The data are corroborated with the WHO, 5 the Center for Disease Control (CDC) in the United States, and the European Center for Disease Control (ECDC).

Based on these figures, the Case Fatality Rate (CFR) is calculated as the number of confirmed deaths divided by the number of confirmed cases, which gives the mortality rate. 6 However, Roser et al. ( 2020 ) caution against taking the CFR numbers at face value to assess mortality risks, 7 because the CFR is based on the number of confirmed cases. Due to limited and sporadic testing capacities, not all COVID‐19 cases can be confirmed. Moreover, the CFR reflects the incidence of the disease in a particular context at a particular point in time. Therefore, CFRs are subject to changes over time and are sensitive to the location and population characteristics.

Recent studies indicate that there are large measurement errors associated with COVID‐19 case numbers. Using data on influenza‐like illnesses (ILI) from the CDC, Silverman et al. ( 2020 ) show that ILIs can be a useful predictor of COVID‐19 cases in the United States. The authors find that there was an escalation in the number of ILI patients during March of 2020. These cases could not be properly identified as COVID‐19 cases due to the lack of testing capabilities during the early stages of the pandemic's progression. The authors suggest that the surge in ILIs may have corresponded to 8.7 million new COVID‐19 cases between March 8 and March 28, most of which were probably not diagnosed. Based on imputation, that figure suggests that almost 80% of all actual cases in the United States during that time period were never diagnosed.

While the dataset mentioned above focuses on counts and tests, the COVID Tracking Project 8 in the United States provides additional data on patients who have been hospitalized, are in intensive care units (ICUs), and are on ventilator support for each of the 50 states. It also grades each state on data quality. Recently, it has included the COVID Racial Data Tracker , 9 which shows the race and the ethnicity of individuals affected by COVID‐19. All of these combined measures and statistics provide a more comprehensive perspective of the spread of the pandemic in the United States.

2.2. Measurement of social distancing

Compared to measuring the spread of the virus, social distancing is not easy to quantify. We determined from the literature that there are three main techniques that are employed: (i) developing and calculating measures of the mobility of the population, (ii) modelling proxies, and (iii) calculating indices. Proxies and indices are based on data related to the observed spread of infection and to the implementation of social distancing policies, respectively. On the other hand, the movements of people are based on their observed travelling patterns. Mobility measures have been used extensively in recent months to discern mobility patterns during the pandemic (Nguyen et al., 2020 ). However, mobility data providers have slight differences in their methodologies. Table  1 provides a summary of how different mobility data providers compile their data.

Social distancing— Mobility measures and how they work

Mobility data are more dynamic and are available at a daily frequency. They can also be used to measure the effect of social distancing on other variables, such as adherence to shelter‐in‐place policies or labor employment patterns (Gupta et al., 2020 ). They also offer key insights into human behavior. For example, “Safegraph” data suggest that social activity in the United States started declining substantially and rapidly well before lockdown measures were imposed (Farboodi et al., 2020 ).

Outside of the United States, a large number of studies have relied on Google LLC Community Mobility Reports. For China, mobility has been mostly measured using data from Baidu Inc. For example, Kraemer et al. ( 2020 ) document how COVID‐19 spread in China using Baidu Inc. data. They investigate travel history from Wuhan to other cities in China, finding that the spatial distribution of cases in other cities was correlated with individual peoples’ travel histories. However, after the implementation of social distancing measures in these cities, the correlation no longer held. Therefore, the authors conclude that local lockdowns rather than travel restrictions helped to mitigate the spread and transmission of COVID‐19 in cities outside Wuhan. See Coelho et al. ( 2020 ) for an examination of the spread of COVID‐19 in Brazil using daily air travel statistics from the Official Airline Guide to measure mobility.

Mobility data do have their own limitations and are not frequently used in the case of epidemics, even though they might be useful (Oliver et al., 2020 ). Mobility data are a proxy for time spent in different locations. They do not allow one to determine the situational context of the contacts that are reported, which are needed to understand the spread of COVID‐19, that is whether they occur in the workplace or in the general community (Martín‐Calvo et al., 2020 ). Those two situations involve different levels of the inherent risk of transmission. In regards to the productive activities of the individuals that are tracked, information on the context is also indeterminate. For those who are working virtually from their homes, for instance, these measures do not capture the value‐added stemming from the time that they allocate to their jobs in the labor market. It is also likely that the quality of these measures can deteriorate when overall unemployment rates and job disruptions are high (Gupta et al., 2020 ). 10 Telecom operator data are deemed to be more representative than locational data, as the former are not limited to people with smartphones, GPS locators, and histories of travel using GPS location (Lomas, 2020 ).

Social media has also been used to measure mobility patterns. Galeazzi et al. (2020) analyze the effect of lockdowns in France, Italy, and United Kingdom on national mobility patterns by exploiting geolocalized data observed from 13 million Facebook users. The authors predictably find that people transition toward localized, short‐ range mobility patterns instead of international, long‐range patterns. However, mobility patterns display heterogeneity across countries. In France and the United Kingdom, mobility is more “concentrated” around huge, central metropolises that are largely disconnected from the provinces, which helps to reduce transmission of the virus. In Italy, on the other hand, the population is more “distributed” across clusters around four major cities that remain interconnected, thus permitting persistent spread.

3. SOCIAL DISTANCING: DETERMINANTS, EFFECTIVENESS, AND COMPLIANCE

A large range of social distancing policies have been implemented, ranging from full‐scale lockdowns to voluntary self‐compliance measures. 11 For example, Sweden imposed relatively light restrictions (Juranek & Zoutman, 2020 ). Large‐scale events were prohibited, and restaurants and bars were restricted to table service only; however, private businesses were generally allowed to operate freely. The population was encouraged to stay at home if they were feeling unwell and to limit social interactions if possible (T.M. Andersen et al., 2020 bib18 ).

People tend to adopt social distancing practices when there is a specific incentive to do so in terms of risk to health and financial cost (Makris, 2020 ). Maloney and Taskin ( 2020 ) attribute voluntary, cooperative actions to either fear of infection or to a sense of social responsibility. Stringent social distancing measures tend to be implemented in countries with a greater proportion of elderly residents, a higher population density, a greater proportion of employees working in vulnerable occupations, higher degrees of democratic freedom, a higher incidence of international travel, and greater distances from the Equator (e.g., Jinjarak et al., 2020 ). Appealing to a game theoretic approach, Cui et al. ( 2020 ) argue that states sharing economic ties will be “tipped” to reach a Nash equilibrium, whereby all other states comply with shelter‐in‐place policies. 12

Social distancing policy determinants have been linked to political party characteristics, political beliefs, and partisan differences (Baccini & Brodeur, 2021 ; Barrios & Hochberg, forthcoming; Murray & Murray, 2020 ). Barrios and Hochberg (forthcoming) correlate the risk perception for contracting COVID‐19 with partisan differences. They find that, in the absence of the imposition of social distancing, counties in the United States which had higher vote shares for Donald Trump are less likely to engage in social distancing. This persists even when mandatory stay‐at‐home measures are implemented across states. Allcott et al. ( 2020 ) find a similar pattern. In addition, the authors show through surveys that Democratic and Republican supporters have different risk perceptions about contracting COVID‐19, and hence hold divergent views regarding the importance of following social distancing measures. These stylized facts make it hard to estimate the causal effect of COVID‐19 on electoral outcomes (Baccini et al., 2021 ).

Researchers are trying to determine the effectiveness of social distancing policies in reducing social interactions and ultimately infections and deaths. Abouk and Heydari ( 2021 ) show that reductions in outside‐the‐home social interactions in the United States are driven by a combination of governmental regulations and voluntary measures, with a strong causal impact for the implementation of statewide stay‐at‐home orders, but more moderate impacts for nonessential business closures and limitations placed on bars/restaurants. Ferguson et al. ( 2020 ) argue that multiple interventions are required in order to have a substantial desired impact on transmission. The optimal mitigation strategy, which is a combination of case isolations, home quarantining, and social distancing of high‐risk groups, would reduce the number of deaths by half and the demand for beds in ICUs by two‐thirds in the United Kingdom and the United States.

Some studies focus on the impact of social distancing on COVID‐19 cases, hospitalizations, etc. For example, Fang et al. ( 2020 ) argue that if lockdown policies had not been imposed in Wuhan, then the infection rates would have been 65% higher in cities outside of Wuhan. Hartl et al. ( 2020 ) show that growth rate of COVID‐19 cases in Germany dropped from 26.7 to 13.8% within 7 days after implementation of lockdowns in the country. Greenstone and Nigam ( 2020 ) project that 3 to 4 months of adherence to social distancing regulations would reduce the number of cases in the United States by 1.7 million by October of 2021, 630,000 of which would translate into averted overcrowding of ICUs in hospitals. Friedson et al. ( 2020 ) argue that early intervention in California helped to reduce significantly the numbers of COVID‐19 cases and deaths during the first 3 weeks following its enactment. Note that this set of interventions falls well short of an economic shutdown.

Similarly, Dave, Friedson, Matsuzawa, Sabia, et al. ( 2020 ) find that counties in Texas that adopted shelter‐in‐place orders earlier than the statewide shelter‐in‐place order experienced a 19 to 26% fall in the rates of COVID‐19 case growth 2 weeks after implementation of such orders. M. Andersen et al. ( 2020 ) find that temporary paid sick leave, a federal mandate enacted in the United States, which allowed private and public employees 2 weeks of paid leave, led to increased compliance with stay‐at‐home orders. On a more global scale, Hsiang et al. ( 2020 ) show that social distancing interventions prevented or delayed around 62 million confirmed cases, corresponding to the aversion of roughly 530 million total infections in China, South Korea, Italy, Iran, France, and the United States within 7 days.

Another important related issue is the determinants of compliance behavior (e.g., Coelho et al., 2020 ; Fan et al., 2020 ). The documented socioeconomic determinants of the degree of compliance with social distancing (lockdowns or safer‐at‐home orders) include, among other factors, income level, trust, and social capital, public discourse, and to some extent, news channel viewership. The degree of ethnic diversity is another documented socioeconomic determinant of social distancing (Egorov et al., 2021 ). Galasso et al. ( 2020 ) rely on survey data from eight OECD countries and provide evidence that women are more likely than men to agree with restrictive public policy measures and to comply with them. Chiou and Tucker ( 2020 ) show that Americans living in higher‐income regions with access to high‐speed internet are more likely to comply with social distancing directives. Coven and Gupta ( 2020 ) find that residents of low‐income neighborhoods in New York City comply less with shelter‐in‐place activities during non‐working hours. According to the authors, this pattern is consistent with the fact that low‐income populations are more likely to be front line, “essential” workers and are also are more likely to make frequent retail shopping visits for essentials, making for two compounded effects. People with lower income levels, less flexible work arrangements (e.g., the inability to work remotely), and a lack of accessible interior space outside of bedrooms are less likely to engage in social distancing (Papageorge et al., 2020 ). Last, Bonaccorsi et al. ( 2020 ) analyze the heterogeneous impacts of lockdowns by socioeconomic conditions of people in Italy. Using mobility data from Facebook, they provide evidence that mobility reduction is higher in municipalities which have stronger fiscal capacity and also those which have lower per‐capita income levels. The authors conclude that the pandemic has disproportionately affected poor individuals within municipalities with strong fiscal capacity in Italy.

Individual beliefs and social preferences should also be taken into consideration, as they affect behavior and compliance. Based on an experimental setup with participants in the United States and the United Kingdom, Akesson et al. ( 2020 ) conclude that individuals overestimated the infectiousness of COVID‐19 relative to expert suggestions. If they were exposed to expert opinion, individuals were prone to correct their beliefs. However, the more infectious COVID‐19 was deemed to be, the less likely they were to undertake social distancing measures. This was perhaps due to the belief that the individual will contract COVID‐19 regardless of his/her social distancing practices. Briscese et al. ( 2020 ) model the impact of “lockdown extension” on compliance using a representative sample of residents from Italy. The authors find that if a given hypothetical extension is shorter than expected (i.e., a positive surprise), the residents are more willing to engage in self‐isolation. Therefore, to ensure compliance, these authors suggest that it is imperative for the government or local authorities to work on communication and to manage peoples’ expectations. Campos‐Mercade et al. ( 2021 ) examine the relationship between social preferences and social distancing compliance. The authors find that people who exhibit prosocial behavior (in this instance individuals who claim that they do not want to expose others to risks) are more likely to follow social distancing measures and other health‐related guidelines.

Bargain and Aminjonov ( 2020 ) demonstrate that residents in European regions with high levels of trust decrease their mobility related to non‐necessary activities compared to regions with lower levels of trust. Brück et al. ( 2020 ) document a negative relationship between being in contact with sick people and trust in people and institutions. Similarly, Brodeur et al. ( 2020 ) find that counties in the United States exhibiting relatively more trust in others decrease their mobility significantly once a lockdown policy is implemented. They also provide evidence that the estimated effect on postlockdown compliance is especially large if people tend to place trust in the media, and relatively smaller if they tend to trust in science, medicine, or government.

Researchers also think about this chain of causality in reverse. Aksoy Eichengreen, and Saka ( 2020 ) find that individuals’ degrees of exposure to epidemics (especially during the ages 18 to 25) has a negative effect on their confidence in political institutions. These individuals are also less likely to have confidence in their health care systems during the times of pandemics. Barrios et al. ( 2021 ) and Durante et al. ( 2021 ) provide evidence that regions with stronger civic culture engaged in more voluntary social distancing. Aksoy, Ganslmeier, and Poutvaara ( 2020 ) find that a high level of public attention (measured through the share of Google shares containing matters related to COVID‐19) has a significant correlation with the timing of implementation of social distancing measures. This relationship is mostly applicable for countries with high quality of institutions. Last, Bartscher et al. ( 2020 ) show that higher levels of social capital (proxied through voter turnout in parliamentary elections) lead to fewer cases per capita accumulated from mid‐March to mid‐May in selected European countries and United Kingdom.

Daniele et al. ( 2020 ) investigate the effect of the COVID‐19 shock on sociopolitical attitudes as opposed to the impact of latter on the spread of the virus. Employing a randomized survey flow design for 8,000 respondents in Germany, Italy, Netherlands and Spain, the authors find that COVID‐19 has led to a deterioration in the levels of interpersonal and institutional trust. It has also lowered support for the European Union in general and for social welfare spending financed by taxes. The authors conclude that these results are driven by the “economic insecurity” rather than the “health” dimensions resulting from the crisis.

Simonov et al. ( 2020 ) analyze the causal effect of cable news viewership on social distancing compliance. The authors examine the average partial effect of Fox News viewership, a news channel that has mostly refuted expert recommendations from leaders of the United States and global public health communities on the severity of COVID‐19 and on compliance, and find that a 1 percentage point increase in Fox News viewership reduced the propensity to stay at home by 8.9 percentage points. Bursztyn et al. ( 2020 ) show that greater exposure to the Hannity show compared to the Tucker Carlson Tonight show in Fox News is associated with larger COVID‐19 case numbers and deaths. This is because the former TV host downplayed the importance of COVID‐19, while the latter provided a serious warning on the same topic during early February. The variation between the messages in the two shows led to changes in behavior in response to COVID‐19.

Table  2 provides a summary of the literature related to the determinants (i.e., factors which influence implementation of social distancing as a policy measure), compliance with social distancing (i.e., whether people are actually following social distancing measures), and their effectiveness (i.e., evidence of success in reducing COVID‐19 cases).

Determinants, compliance and effectiveness of social distancing measures: Summary of studies

4. MACROECONOMIC IMPACTS AND PLAUSIBLE MECHANISMS

4.1. plausible mechanisms for macroeconomic impact.

To understand the potential negative economic impact of COVID‐19, it is important to comprehend the economic transmission channels through which the shocks will adversely affect the economy. According to Carlsson‐Szlezak et al. ( 2020a , b ), there are three main transmission channels. The first is the direct impact, which is related to reduced consumption of goods and services. Prolonged lengths of the pandemic and the concomitant social distancing measures might reduce consumer confidence by keeping consumers at home, wary of discretionary spending, and pessimistic about their long‐term economic prospects. The second one is the indirect impact working through financial market shocks and their effects on the real economy. Household wealth will likely fall, savings will increase, and consumption spending will decrease further. The third consists of supply‐side disruptions; as restrictions halt or hamper production activities, they will negatively impact supply chains, labor demand, and employment, leading to prolonged periods of lay‐offs and rising unemployment. In particular, Baldwin ( 2020 ) discusses the expectation shock by which a “wait‐and‐see” attitude is adopted by economic agents. The author argues that this is common during economic climates characterized by uncertainties, as there is less confidence in markets and in engaging in economic transactions. Ultimately, the intensity of the shock is determined by the underlying epidemiological properties of the virus, consumer behaviour, and firm behavior in the face of adversity and uncertainty, and public policy responses. To understand the implications of the spread of the virus and the consequent social distancing measures on economic activities, a number of researchers have integrated canonical epidemiology models such as the susceptible, infected, resolved model (SIR) with macroeconomic models (see the Online Appendix for a detailed review of these models).

Gourinchas ( 2020 , p. 33) summarizes the effect on the economy by stating: “A modern economy is a complex web of interconnected parties: employees, firms, suppliers, consumers, and financial intermediaries. Everyone is someone else's employee, customer, lender, etc.” Due to the very high degrees of interconnectiveness and specialization of productive activities, a breakdown in the supply chains and the circular flows will have cascading effects. Baldwin ( 2020 ) describes the impact of COVID‐19 and subsequent social distancing measures on the macroeconomy within a circular flow framework.

It is also important to understand the processes that generate recoveries from economic crises. Carlsson‐Szlezak et al. ( 2020a ) explain different types of recovery in the aftermath of negative shocks through the concept of “shock geometry.” There are three broad scenarios of economic recoveries, which we mention in ascending order of their severity. First, there is the most optimistic one labelled “V‐shaped,” whereby aggregate output is displaced and quickly recovers to its pre‐crisis path. Second, there is the “U‐shaped” path, whereby output drops swiftly but does not return swiftly to its precrisis path. The gap between the former trajectory of output and the actual one remains large for quite some time, but recovery eventually occurs. Third, in the case of the very grim “L‐shaped” path, output drops and reaches a trough, but subsequent growth rates remain very low. The gap between the former and the new output paths continues to widen. Another scenario of economic recovery often mentioned is the “K‐shaped” one, which occurs when, following a recession, different parts of the economy recover at different rates, times, or magnitudes.

Carlsson‐Szlezak et al. ( 2020b ) state that after previous pandemics, such as the 1918 Spanish Influenza, the 1958 Asian Influenza, the 1968 Hong Kong influenza, and the 2002 SARS outbreak, economies have tended to experience “V‐shaped” recoveries. However, the pattern for the COVID‐19 economic recovery is not expected to be straightforward. This is because the effects on employment due to social distancing measures and lockdowns are expected to be much larger. According to Gourinchas ( 2020 ), during a short period, as much as 50% of the working population might not be able to find work. Moreover, even if no containment measures are implemented, a recession would occur anyway, fueled by the precautionary and/or risk‐averse behavior of households and firms faced with the uncertainty of dealing with a pandemic as well as with an inadequate public health response (Gourinchas, 2020 ).

Guerrieri et al. ( 2020 ) show that in a multisector model with certain assumptions, such as incomplete markets, low substitutability across sectors, and liquidity‐constrained consumers, COVID‐19 imparts a supply shock which works through lockdowns, layoffs, firm closures, etc. The subsequent impact would be a drop in aggregate demand and a demand‐deficient recession, that is, a “Keynesian supply shock.”

Baqaee and Farhi ( 2020 ) analyze the impact through a disaggregated Keynesian model comprised of multiple sectors, factors of production, and input‐output linkages with different features, such as nominal wage rigidities and credit constraints. They find that negative supply shocks are stagflationary, whereas negative demand shocks are deflationary. The policy implications are somewhat ambiguous. Policies that boost aggregate supply (e.g., providing subsidies to businesses, relaxing lockdowns, etc.) might not be effective in increasing demand in certain demand‐constrained sectors. Similarly, demand‐inducing policies (e.g., lower interest rates, more generous social insurance, etc.) might lead to supply shortages and inflationary pressures in certain sectors.

4.2. Quantitative macroeconomic impacts

As the pandemic unfolds, many researchers have been thinking about the economic impact from a historical perspective. Ludvigson et al. ( 2020 ) try to quantify the macroeconomic impact of costly disasters (natural and manmade) and translate them into estimates of the impact of COVID‐19. They find that in a fairly conservative scenario, pandemics, such as COVID‐19, are tantamount to large, multiple‐period exogenous shocks. Using a “costly disaster” index, the authors find that COVID‐19 is constituted of multi‐period shocks in the United States, which leads to a 12.75% drop in industrial production, a 17% loss in service employment, sustained and drastic reductions in air travel, and macroeconomic uncertainties which linger for up to 5 months. Jordà et al. ( 2020 ) analyze the rate of return on the real natural interest rate (the level of real returns on safe assets resulting from the demand and supply of investment capital in a noninflationary environment) from the 14 th century to 2018. Theoretically, a pandemic is supposed to induce a downward negative shock to the real natural interest rate. This is because investment demand decreases due to excess capital per labor unit (i.e., a scarcity of labor being utilized), while savings flows increase due to either precautionary reasons or to replace lost wealth. The authors find that the natural rate of interest may be about 2 percentage points lower than it would otherwise have been some 20 years after the pandemic, and only return to counterfactual levels after 40 years.

Analysis based on historical data, however, might not be relevant in this case. According to Baker et al. ( 2020 ), COVID‐19 has led to massive spikes in uncertainty, and there are no close historical parallels. Because of the speed of evolution and timely requirements of data, the authors suggest that one should utilize forward‐looking uncertainty measures to ascertain its impact on the economy. They formulate the uncertainty measure from the Standard & Poor's 500 Volatility Index (VIX) and the news‐based economic policy uncertainty (EPU) index developed by Baker et al. ( 2016 ). Using a real business cycle model, the authors find that a COVID‐19 shock leads to a year‐over‐year contraction of GDP by 11% in fourth quarter of 2020. According to the authors, more than half of the contraction is caused by COVID‐19‐induced uncertainty. Based on a similar approach, Altig et al. ( 2020 ) conduct an analysis of different forward‐looking uncertainty measures during the pandemic. Coibion et al. ( 2020a ) use surveys to assess the macroeconomic expectations of households in the United States. They find that it is primarily lockdowns, rather than the infections themselves, that lead to declines in consumption spending and employment, lower inflationary expectations, increased uncertainty, and lower mortgage payments being made.

Eichenbaum et al. ( 2020 ) model the interactions between economic decisions and the spread of the virus. They find that, without any mitigation measures, aggregate consumption falls by 9.3% over a 32‐week period. On the other hand, labor supply or hours worked follow a U‐shaped pattern, with a peak decline of 8.25% in the 32nd week from the start of the pandemic. These reductions decrease peak infection rates and death tolls from 7% and 0.30% to 5% and 0.26% respectively, but worsen the magnitude of the recession. Infected people fail to internalize the impact of their choices on the spread of the virus. Therefore, the optimal containment policy increases the severity of the recession but saves lives. 13

Mulligan ( 2020 ) assesses the opportunity cost of “shutdowns” in order to document the macroeconomic impact of COVID‐19. Within the National Accounting Framework for the United States, the author extrapolates the welfare loss stemming from “nonworking days,” the fall in the labor‐capital ratio resulting from the absence/layoff of workers, and the resulting idle capacity of workplaces. After accounting for dead‐weight losses stemming from fiscal stimulus, the replacement of normal import and export flows with black market activities, and the effect on nonmarket activities (lost productivity, missed schooling for children and young adults), the author finds the welfare loss to be approximately $7 trillion per year of shutdown. Medical innovations, such as vaccine development, contact tracing, and workplace risk mitigation can help to offset the welfare loss by around $2 trillion per year of shutdown.

Other researchers have examined the supply side. Bonadio et al. ( 2020 ) use a quantitative framework to simulate a global lockdown as a contraction in labor supply for 64 countries. The authors find that the average decline in real GDP constitutes a major contraction in economic activity, with a large share attributed to disruptions in global supply chains. Elenev et al. ( 2020 ) model the impact of COVID‐19 as a fall in worker productivity and as a decline in labor supply, which both adversely affect firm revenue. The fall in revenue and the subsequent non‐repayment of debt‐servicing obligations spur a wave of corporate defaults, which might also bring down financial intermediaries. Céspedes et al. ( 2020 ) formulate a minimalist economic model in which the virus also leads to losses in productivity. The authors predict a vicious cycle triggered by the loss of productivity causing lower collateral values, in turn limiting the amount of borrowing activity, subsequently leading to decreased employment, followed by a further decline in productivity. The shock is thus magnified through an “unemployment and asset price deflation doom loop” (see Fornaro & Wolf, 2020 ).

Consumption pattern responses and debt responses from pandemic shocks had not been analyzed prior to COVID‐19 (Baker et al., 2020 ). Using transaction‐level household data, these authors find that households sharply increased their spending during the initial period in specific sectors such as retail and food spending. These increases, however, were followed by a decrease in overall spending. Similarly, Chang and Meyerhoefer ( 2020 ) show that consumers in Taiwan have increased food purchases from online platforms. Binder ( 2020 ) conduct an online survey of 500 United States consumers to investigate their concerns and responses related to COVID‐19, which indicated those items of consumption on which they were spending either more or less. They find that 28% of the respondents in that survey postponed future travel plans, and that 40% forewent food purchases. Interestingly, Binder ( 2020 ) finds from the surveys that consumers tend to associate graver concerns about COVID‐19 with higher inflationary expectations, a sentiment which serves as a proxy for “pessimism” or “bad times.”

Clemens and Veuger ( 2020 ) focus on the declines in government sales and income tax collections across US states. According to the authors, COVID‐19 has led to a substantial decline in consumption levels compared to income levels. This pattern is unlike the case in previous recessions, during which income decreased more than consumption. The authors find that the COVID‐19 pandemic will reduce the states’ tax collections by $42 billion in the second quarter of 2020. For fiscal year 2021, the authors anticipate an overall decline in sales and income tax revenues of $106 billion with heterogenous losses across US states.

McKibbin and Fernando ( 2020 ) estimate the aggregate economic costs. Using a hybrid DSGE/CGE global model, the authors model COVID‐19 as a negative shock to labor supply, consumption spending, financial markets, but as a positive shock to government expenditure, particularly stemming from health‐related expenditures. The authors outline seven different scenarios and provide a range of estimates of the increase in mortality and the fall in GDP for a number of countries across the world. In the case of the most contained outbreak, the number of deaths reaches around 15 million, while the reduction in global GDP is around $2.4 trillion in 2020.

Eppinger et al. ( 2020 ) use a quantitative international trade model with input‐output linkages for 43 countries to assess the impact of COVID‐19 supply shock on global value chains. They find that due to the supply shock, China experienced a welfare loss of 30% with moderate (positive or negative) spillover to other countries. Estimating a simulation consisting of a counterfactual scenario described as “without global value chains,” the authors find that welfare losses are reduced for some countries by as much as 40%, while they are magnified for others.

The economic impact of shocks, such as pandemics, is usually measured with aggregate time series data. However, these datasets are available only after a certain lag. In order to analyze the economic impact at a higher frequency, Lewis et al. ( 2020 ) developed a weekly economic index (WEI) using 10 different economic variables to track the economic impact of COVID‐19 in the United States. These authors report that between March 21 and March 28, the WEI declined by 6.19%. This was driven by a decline in consumer confidence, a fall in fuel sales, a rise in unemployment insurance (UI) claims, and changes in other variables. Similarly, Demirguc‐Kunt et al. ( 2020 ) estimate the economic impact of social distancing measures via three high‐frequency proxies (electricity consumption, nitrogen dioxide emissions, and mobility records). The authors find that social distancing measures led to a 10% decline in economic activity (as measured by electricity usage and emissions) across European and Central Asian countries between January and April. Chetty et al. ( 2020 ) develop a real‐time economic tracker using daily statistics on consumption, employment, business revenue, job postings, and other variables. The authors show that the initial slowdown in economic activity was partly driven by reductions in consumption by high‐income individuals. These spending shocks negatively affected business revenues catering to high‐income individuals. Subsequently, low‐income individuals working for these businesses lose much of their incomes and reduce their consumption levels. Kapteyn et al. ( 2020 ) tracked a representative sample of 7,000 respondents in Los Angeles County, California every 2 weeks to assess the impact of COVID‐19 over time.

Brinca et al. ( 2020 ) estimate the labor demand and supply shocks occurring in different sectors in the US economy employing a Bayesian structural vector autoregression model. They find that the decrease in work hours can be attributed to negative labor supply shock, a result that they suggest has important policy design implications. A negative labor supply shock is directly related to the lockdown and might be mitigated once such policies are lifted.

5. SOCIOECONOMIC CONSEQUENCES OF COVID‐19

We now review studies documenting the socioeconomic consequences of COVID‐19 and the ensuing lockdowns. Social distancing and lockdown measures have been shown to adversely affect labor markets, mental health and well‐being, racial inequality, and gender‐related outcomes. The environmental implications, while likely to be positive overall, also deserve careful analysis.

5.1. Labor market outcomes

A large number of studies document the effects on the variables of hours of work and job losses (e.g., Kahn et al., 2020 ). The major increases in unemployment observed in the United States are driven partly by lockdowns and social distancing policies (Rojas et al., 2020 ). Accounting for cross‐state variation in the timing of business closures and stay‐at‐home mandates in the United States, Gupta et al. ( 2020 ) find that the employment rate in the United States falls by about 1.7 percentage points for every extra 10 days that a state experienced a stay‐at‐home mandate during the period of March 12th to April 12th.

Coibion et al. ( 2020b ) find that the level of unemployment and job losses in the United States is more severe than one might judge based on the rise in UI claims, which is to be expected given the low coverage rate of the UI regimes in the United States. They also project a severe fall in the labor participation rate in the long run accompanied by an increase in the number of “discouraged workers” (jobless workers who have stopped actively searching for work, effectively withdrawing from the labor force). This phenomenon might be due to the disproportionate impact of COVID‐19 on the older population. Aum et al. ( 2020a , 2021b ) find that an increase in infections leads to a drop in local employment even in the absence of lockdowns in South Korea, whose government did not mandate them. This estimated impact was higher for countries, such as the United States and the United Kingdom, where mandatory lockdown measures were imposed.

Adams‐Prassl et al. ( 2020a ) analyze the inequality of the distributions of job and income losses based on the type of job held and on individual characteristics for the United States and the United Kingdom. The authors find that workers who can perform none of their employment tasks from home are more likely to lose their job. This study also finds that younger individuals and people without a university education were significantly more likely to experience drops in their income. Yasenov ( 2020 ) finds that workers with lower levels of education, younger adults, and immigrants are concentrated in occupations whose tasks are less likely to be performed from home. Similarly, Alstadsæter et al. ( 2020 ) find that the pandemic shock in Norway has a strong socioeconomic gradient, as it has disproportionately affected the financially vulnerable population, including parents with younger children.

Béland, Brodeur, and Wright ( 2020 ) discuss heterogeneous effects across occupations and workers in the United States, showing that occupations that have a higher share of workers working remotely were less affected by COVID‐19. On the other hand, occupations with relatively more workers working in proximity to others were more affected. They also find that occupations classified as “more exposed to disease” are less affected, which is possibly due to the number of essential workers in these occupations. Based on these results, it can be reasonably expected that workers might change (or students might select different) occupations in the medium term. Bui et al. ( 2020 ) focus on the impact of COVID‐19 on older workers in the United States. Using CPS data, they show that older workers who are over 65 years of age, especially women, are facing higher unemployment in this COVID‐19 recession compared to previous ones.

Kahn et al. ( 2020 ) show that firms in the United States dramatically reduced job vacancies from the second week of March 2020 and thereafter. The authors find that the job vacancy declines occurred simultaneously with increasing UI claims. Notably, the labor market declines (proxied through reductions in job vacancies and increases in UI claims) were uniform across states, with no notable differences across states which experienced the spread of the pandemic, or implemented stay‐at‐home orders, earlier than others. The study also finds that the reductions in job vacancies were uniform across industries and occupations, except for those in front line jobs, such as nursing. Baert et al. ( 2020a ) investigate the impact of COVID‐19 on career prospects through surveys conducted in Belgium. They document concerns that were expressed about job losses and missing out on promotions, especially among migrant workers.

Fairlie ( 2020 ) analyzes the impact of COVID‐19 on the number of small businesses in the United States. Using the April 2020 CPS data, the author finds that the number of active business owners declined by 22% between February and April 2020. While most major industries faced large drop in business, the authors also find that female and immigrant‐owned businesses were disproportionately affected.

With the enforcement of social distancing measures, work from home has become increasingly prevalent. The degree to which economic activity is impaired by such social distancing measures depends largely on the capacity of firms to maintain business processes from the homes of workers (Alipour et al. 2020 ; Papanikolaou & Schmidt, 2020 ). Additionally, working from home or working remotely are much more common and are thought to cause lower productivity losses in industries that are staffed by better educated and better paid workers (Bartik et al., 2020 ). Brynjolfsson et al. ( 2020 ) find that the increase in cases per 100k individuals is associated with a significant rise in the fraction of workers switching to remote work and the fall in the fraction of workers commuting to work in the United States. Interestingly, the authors find that people working from home are more likely to claim UI (if they are laid off) than people who are still commuting to work and are likely working in industries providing essential services.

Dingel and Neiman ( 2020 ) analyze the feasibility of jobs that can be done from home. They find that 37% of jobs can be feasibly performed from home. A different but related context for the feasibility of work from home is the extent to which the job involves face‐to‐face (F2F) interaction. According to Avdiu and Nayyar ( 2020 ), the job‐characteristic variables of home‐based work (HBW) and F2F interaction differ along three main dimensions, namely: (i) temporal (short run vs. medium run); (ii) the primary channel of effects (supply and demand of labor for the occupation/tasks); and (iii) the relevant margins of adjustment (intensive vs. extensive). They argue that the supply of labor in industries with HBW capabilities and low F2F interactions (e.g., professional, scientific, and technical services) might be the least affected. Nevertheless, those industries and occupations with HBW capabilities and high F2F interactions are likely to experience negative productivity shocks. As lockdown restrictions are lifted, industries with low HBW capabilities and low F2F interactions (e.g., manufacturing, transportation, and warehousing) might be able to recover relatively quickly. The risk of infection through physical proximity can be mitigated by wearing personal protective equipment (PPE) and by taking other relevant precautionary measures. However, those industries with low HBW capabilities and high F2F interactions (e.g., accommodation and food services, arts entertainment and recreation) are likely to experience slower recoveries, as consumers might be apprehensive about patronizing them, for example, cinemas and restaurants. Using a web survey in Belgium, Baert et al. ( 2020b ) find that a majority of respondents thought that teleworking and digital conferencing were here to stay and will become more common in the postpandemic period.

From the firm's perspective, there are large short‐term effects of temporary closures, such as the (perhaps permanent) loss of productive workers and declines in job postings, all of which are characterized by strong heterogeneity across industries. Bartik et al. ( 2020 ) survey a small number of firms in the United States and document that several of them have temporarily closed shop and reduced their number of employees compared to January 2020. The surveyed firms were not optimistic about the efficacy of the fiscal stimulus implemented by the federal government of the United States. Campello et al. ( 2020 ) find that job losses have been more severe for industries with highly concentrated labor markets (i.e., where hiring is dominated by a few employers), nontradable sectors (e.g., construction, health services), and credit‐constrained firms. Hassan et al. ( 2020 ) discern a pattern of heterogeneity with respect to firm resilience across industries around the World. Based on earnings call reports, they provide evidence that some firms are expecting increased business opportunities in the midst of the global disruption (e.g., firms which make medical supplies or others whose competitors are facing negative impressions after the outbreak due to their association with regions where case numbers are high). Barrero et al. ( 2020 ) measure the reallocation of labor in response to the pandemic‐induced demand response (e.g., increased hiring by delivery companies, delivery‐oriented restaurant/fast food chains, technology companies).

To conclude this subsection, a large number of studies try to predict labor market outcomes by exploiting high frequency data (e.g., Adams‐Prassl et al. 2020a , Chetty et al., 2020 ). For instance, Bartik et al. ( 2020 ) and Kurmann et al. ( 2020 ) rely on worker‐firm matched daily data drawn from “Homebase,” a scheduling and time clock software provider, to construct real‐time data for small businesses. Other studies have also used high‐frequency electricity market data to estimate the short‐run impacts of COVID‐19 on economic activity (e.g., Fezzi & Fanghella, 2020 ).

5.2. Health outcomes

The impact of the pandemic on physical health and mortality has been documented in many studies (e.g., Goldstein & Lee, 2020 ; Lin & Meissner, 2020 ). Knittel and Ozaltun ( 2020 ) document a positive correlation between the share of elderly population, the incidence of commuting via public transportation, and the number of COVID‐19 deaths in the United States. In contrast, the authors provide evidence that obesity rates, the number of ICU beds per capita, and poverty rates are not related to the death rate. Chatterji and Li ( 2021 ) document the effect of the pandemic on the US health care sector. The authors find that it is associated with a 67% decline in the total number of outpatient visits per provider by the week of April 12th‐18th 2020 relative to the same week in prior years. This might have negative health consequences, especially among individuals with chronic health conditions. Hermosilla et al. ( 2020 ) show that COVID‐19 has crowded out non‐COVID‐19‐related health care demands in China. Others, such as Alé‐Chilet et al. ( 2020 ), explore the drop in emergency cases in hospitals around the world.

Nevertheless, during a crisis, such as the COVID‐19 pandemic, it is common for everyone to experience increased levels of distress and anxiety, particularly the sentiment of social isolation (American Medical Association, 2020 ). A growing number of studies document worsening mental health status and levels of well‐being (Adams‐Prassl et al. 2020b ; Brodeur, Clark, Fleche, & Powdthavee, 2021 ; Davillas & Jones, 2020 ; de Pedraza et al., 2020 , and Tubadji et al., 2020 ). According to Lu et al. ( 2020 ), social distancing or lockdown measures are likely to affect psychological well‐being through a lack of access to essential household supplies, discriminatory treatment, or exclusion by neighbors. They assert that maintaining a positive attitude (in terms of severity perceptions, the credibility of real‐time updates of information, and confidence in social distancing measures) can help reduce depression. Hamermesh ( 2020 ) also provides evidence that, adjusted for numerous demographic and economic variables, happiness levels during the COVID‐19 pandemic are affected by how people spend time and with whom. In the opposite case, using an experimental set‐up, Bogliacino et al. ( 2020 ) find that a negative shock triggered by COVID‐19 lowers cognitive functionality and increases risk aversion and the propensity to punish others, that is negative reciprocity. Public mental health is also affected by the cognitive bias related to the diffusion of public death toll statistics (Tubadji et al., 2020 ). These needs are all the less likely to be addressed given the lower levels of provision of health care and social work services.

Using the Canadian Perspective Survey Series, Béland, Brodeur, Mikola, and Wright ( 2020 ) find that those who missed work not due to COVID‐19, and those who were already unemployed, showed declines in mental health. Using panel data in the United Kingdom, Etheridge and Spantig ( 2020 ) report a large deterioration in the state of mental health, with much larger effects for women.

The implementation of lockdown policy also adversely affected public mental health. Armbruster and Klotzbücher ( 2020 ) demonstrate that there were increases in the demand for psychological assistance (through helpline calls) due to lockdown measures imposed in Germany. The authors find that these calls were mainly driven by mental health issues such as loneliness and depression. Brodeur, Clark, Fleche, & Powdthavee et al. ( 2021 ) show that there has been a substantial increase in the search intensity on Google for “boredom” and “loneliness” during the postlockdown period in nine Western European countries and the United States during the first few weeks of lockdowns. Using experimental surveys, Codagnone Bogliacino, Gómez, and Charris et al. ( 2020 ) find that about 43% of the population in Italy, Spain, and United Kingdom are at high risk of developing mental health problems; not only because of the negative economic shock, but also due to conditions of long‐standing economic weakness and vulnerability in those countries.

Fetzer et al. ( 2020 ) find that there has been broad public support for COVID‐19 containment measures. However, some of the respondents believe that the general public fails to adhere to health measures, and that the governmental response has been insufficient. These respondents have a tendency to exhibit a poorer state of mental health. If governments are seen to take decisive actions, however, then the respondents altered their perceptions about governments and other citizens, which in turn improved their state of mental health.

5.3. Gender and racial inequality

A growing literature points out that COVID‐19 has had an unequal impact between genders and across races in OECD countries; specifically, women and racial minorities, such as African‐Americans and Latinos, have been unduly and adversely affected. While it is thought that prior recessions typically affected men more than women, many studies provide evidence that COVID‐19 has large negative effects on women's labor market outcomes (Adams‐Prassl et al., 2020a ; Forsythe et al., 2020 ; Yasenov, 2020 ). Alon et al. ( 2020 ) argue that women's employment is concentrated in sectors such as health care and education. Moreover, the closure of schools and daycare centers led directly to increased childcare needs, which would have a negative impact on working and/or single mothers. For example, based on household surveys in Spain, Farré et al. ( 2020 ) find that while men increased their participation in household work and childcare duties during lockdowns, the burden of these tasks fell disproportionately on women.

Couch et al. ( 2020a ) examine the variation in unemployment shocks among minority groups in the United States. The authors find that Latino groups were disproportionately affected by the pandemic. They attribute the difference to an unfavorable occupational distribution (e.g., Latino workers tend to work in nonessential services) and to lower skill levels among them. Borjas and Cassidy ( 2020 ) determine that the COVID‐19 shock led to a fall in employment rates of immigrant men compared to native men in United States, which was in contrast to the historical pattern observed during previous recessions. The immigrants’ relatively high rate of job loss was attributed to the fact that immigrants were less likely to hold jobs that could be performed remotely from home. The likelihood of being unemployed during March 2020 was significantly higher for racial and ethnic minorities in the United States (Montenovo et al., 2020 ). In a similar vein, McLaren ( 2020 ) finds that minorities’ population shares in a county strongly correlate with COVID‐19‐related deaths in the United States. After controlling for the factors of education, jobs, and travel patterns, the correlation holds for the African‐American and the First‐Nations populations. The author shows that these racial disparities between African‐Americans, First Nations peoples, and others can be partially attributed to differentials in public transit usage patterns.

Couch et al. ( 2020b ) find that COVID‐19 has unduly affected women compared to men in the United States. Using employment to population ratios and number of hours from the CPS data, the authors find that women with school‐age children faced greater declines in employment and work hours compared to men between April and August 2020. The reductions in work hours and employment can be explained by additional childcare responsibilities, job and skill characteristics, and lower numbers of women involved in “essential” industries.

Schild et al. ( 2020 ) find that COVID‐19 occasioned a rise of Sino‐phobia across the internet, particularly when western countries started showing signs of infection. Bartos et al. ( 2020 ) document the causal effect of economic hardships on hostility against certain ethnic groups in the context of COVID‐19 using an experimental approach. The authors find that the COVID‐19 pandemic magnifies sentiments of hostility and discrimination against foreigners, especially those from Asia.

5.4. Environmental outcomes

The global lockdown and the considerable slowdown of economic activities are expected to have a positive effect on the environment (Almond et al., 2020 ; Cicala et al., 2020 ). He et al. ( 2020 ) show that lockdown measures in China led to a remarkable improvement in air quality. The Air Quality Index and the fine particulate matter (PM 2.5 ) concentrations were brought down by 25% within weeks of the lockdown, with larger effects recorded in colder, richer, and more industrialized cities. Similarly, Almond et al. ( 2020 ) focused on air pollution and the release of greenhouse gases in China during the post‐COVID‐19 period. They determined that, while nitrogen dioxide (NO 2 ) emissions fell precipitously, sulphur dioxide emissions (SO 2 ) did not decrease. For China as a whole, PM 2.5 emissions fell by 22%; however, ozone concentrations increased by 40%. These variations show that there is not necessarily an unambiguous improvement in air pollution due to the economic slowdown. The reduction can be attributed to less travel in personal vehicles causing lower nitrous oxide (NO 2 ) emissions.

Brodeur, Cook, & Wright ( 2021 ) examine the causal effect of “safer‐at‐home” policies on air pollution across US counties. They find that “safer‐at‐home” policies decreased air pollution (measured as PM 2.5 emissions) by almost 25% on average, with larger effects for populous counties. Cicala et al. ( 2020 ) focus on the health and mortality benefits of reduced vehicle travel and electricity consumption in the United States due to stay‐at‐home policies, suggesting that reductions in emissions from less travel and from lower electricity usage reduced deaths by over 360 per month.

On the other hand, Andree ( 2020 ) focuses on the effect of pollution on cases, finding that PM 2.5 levels are a highly significant predictor of COVID‐19 incidence using data from 355 municipalities in the Netherlands. In terms of COVID‐19‐related deaths, Knittel and Ozaltun ( 2020 ) find no evidence that pollution levels are related to death rates in the United States.

Based on the research discussed in section  5 above, Table  3 provides a summary of these strands of the literature dealing with the socioeconomic and environmental outcomes resulting from social distancing actions, stay‐at‐home orders, and/or lockdowns including a listing of the statistical measures and methodologies that were utilized.

Socioeconomic outcomes of COVID‐19 lockdowns: Summary of studies

6. POLICY MEASURES

The economic literature deals with a wide assortment of policy measures. We organize our presentation into six broad topics: (i) the types of policy measures, (ii) the determinants of government policy, (iii) optimal testing methods, (iv) the lockdown measures and their associated factors, (v) the lifting of the lockdown measures, and (vi) the economic stimulus measures.

To mitigate the negative effects of public health controls on the economy and to sustain and promote public welfare, governments all around the World have implemented a variety of policies within a very short time frame. These include fiscal, monetary, and financial policy measures (Gourinchas, 2020 ). The economic measures vary across counties in terms of breadth and scope, and they target households, firms, health systems, and/or banks (Weder di Mauro, 2020 ).

Using a database of economic policies implemented by 166 countries, Elgin et al. ( 2020 ) employ the technique of Principal Component Analysis (PCA) to develop their COVID‐19 Economic Stimulus Index. The authors correlate the standardized index with predictors of governmental response, such as population characteristics (e.g., median age), public health‐related measures (e.g., the number of hospital beds per capita), and economic variables (e.g., GDP per capita). They find that the economic stimulus is larger for countries with higher COVID‐19 infections, older median ages, and higher GPD‐per‐capita levels. In addition, the authors develop a “Stringency Index,” which includes measures such as school closures and travel restrictions. They find that the “Stringency Index” is not a significant predictor of their economic stimulus index, which suggests that public health measures do not drive economic stimulus measures (Weder di Mauro, 2020 ).

On a similar note, Porcher ( 2020 ) has created an index of public health measures using the PCA technique. The index is based on 10 common public health policies implemented across 180 countries to mitigate the spread of COVID‐19. The index is designed to measure the stringency of the public health response across countries. The author finds that, abstracting from the COVID‐19 case numbers and deaths, countries which have better public‐health systems and effective governance tend to have less stringent public health measures.

C. Cheng et al. ( 2020 ) develop the “CoronaNet–COVID‐19 Government Response Database,” which accounts for policy announcements made by countries globally since 31 December 2019. The information that is contained in this data base is categorized according to: (i) type of policy, (ii) national versus subnational enforcement, (iii) people and geographic region targeted by the policy, and (iv) the time frame within which it is implemented. Table  4 provides a description of the government response database for 125 countries. Counts are tabulated according to 15 types of interventions for two variables: cumulative number of policies (of that type) implemented and the number of countries which have implemented it. It also displays that average value for the degree of enforcement.

Summary statistics of COVID‐19 government response dataset

Source: C. Cheng et al. ( 2020 ).

There is substantial variation across policy measures. The policy most governments have implemented is “external border restriction,” that is, restricting access to entry through ports. It has been imposed by 186 countries; the second most common policy measure, implemented by 169 countries, is “school closures.” However, in terms of the frequency of implementation across all countries, the type of “obtaining or securing health resources” has the highest level. This includes the provision of materials (e.g., face masks), personnel (e.g., doctors, nurses), and infrastructure (e.g., hospitals). The second most frequently implemented policy is “restrictions on nonessential businesses.” In terms of stringency of policy enforcement, “emergency declaration” and the formation of a “new task force” or an “administrative reconfiguration to tackle pandemic” are implemented with 100% stringency.

Due to these major differences between policy responses across countries and over time, the authors use a dynamic Bayesian item‐response approach to measure the implied economic, social, and political cost of implementing a particular policy over time. They also develop a supplementary measure labelled the “Policy Activity Index,” which assigns a higher rank for policy measures to countries that are more willing to implement a “costly” policy. Based on that index, the authors determine that school closure is the costliest to implement followed by mandatory business closure and social distancing policies. Moreover, internal border restrictions are viewed as more costly compared to external border restriction.

The topic of optimal testing methods has received a great deal of attention in the media and, to some extent, in academia. A well‐known proposal defended by Paul Romer and many others is a comprehensive “test and isolate” policy, which would effectively reduce the effective reproduction number and allow the economy to operate more openly. 14 Taipale et al. ( 2020 ) formalize this proposal and argue that the epidemic would collapse at a sufficient rate of testing and isolation, and that concurrent testing would outperform random sampling of individuals. Other proposals for optimal testing include regular testing of people in groups that are more likely to be exposed to COVID‐19 (e.g., Cleevely et al., 2020 ; Gollier & Gossner, 2020 ), multi‐stage group testing (e.g., Eberhardt et al., 2020 ), and testing on exit from quarantine instead of upon entry (e.g., Wells et al. 2020 ).

The topic of optimal lockdown policies has been investigated mostly by using epidemiology macroeconomic models, some of which are oriented around the dichotomy between the case in which the choices (and responses) are all made by private agents and the case in which the choices are made by a social planner (Acemoglu et al., forthcoming; Alvarez et al., forthcoming; Berger et al., forthcoming; Bethune & Korinek 2020 ; Eichenbaum et al., 2020 ). Jones et al. ( 2020 ) argue that in contrast to private agents, the social planner will seek to front‐load mitigation strategies: that is to impose strict lockdowns from the beginning to reduce the spread of infection and let the economy to fall into a deep recession. This is because their model's set‐up not only considers the concomitant health care costs and congestion in hospitals, but also rightly considers the fact that workers need time to become productive for a work‐from‐home situation. The outcomes are dependent on the assumed values of the parameters that are inputted into these models. The optimal policy choice reflects the rate of time preference, epidemiological factors, the value of statistical life, the rate at which death rate increases in the infected population, the hazard rate for a vaccine discovery, the learning effects in the health care sector, and the severity of output losses due to a lockdown (Gonzalez‐Eiras & Niepelt, 2020 ). The intensity of the lockdown depends on the gradient of the fatality rate as a function of the number of infected individuals and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown and shortens the duration of the optimal lockdown (Alvarez et al., forthcoming). Chang and Velasco ( 2020 ) argue that the optimality of policies depends on peoples’ expectations. For instance, fiscal transfers must be large enough to induce people to stay at home in order to reduce the degree of contagion; otherwise they might not change their behavior in efforts to reduce the risk of infection. Their analysis also contains a critique of the use of SIR models, as the parameters used in that class of models (which remain fixed in value) would shift as individuals change their behavior in response to policy. Kozlowski et al. ( 2020 ) investigate the scarring effect on perceptions (i.e. the change in belief about the probability of an extreme but negative or tail‐risk event) of COVID‐19, and find that revisions in belief about tail‐risk events among economic agents will lead to a larger and more persistent negative impact on the economy in the long run.

When the daily death rates and case numbers decline, policies regarding reopening the economy are of primary importance. Gregory et al. ( 2020 ) describe the lockdown measure as a “loss of productivity,” whereby relationships between employers and laborers are suspended, terminated, or continued. They further explain that postpandemic, the speed and the type (V‐shaped or L‐shaped) of recovery depend on at least three factors: (i) the fraction of workers who, at the beginning of the lockdown, enter unemployment while maintaining a relationship with their employer, (ii) the rate at which inactive relationships between employers and employees dissolve during the lockdown, and (iii) the rate at which workers who, at the end of the lockdown, are not recalled by their previous employer can find new, stable jobs elsewhere (Gregory et al., 2020 ).

Harris ( 2020 ) points out the importance of utilizing several status indicators (e.g., results of partial voluntary testing, guidelines for eligibility of testing, daily hospitalization rates) in order to decide upon the course of action on reopening the economy. Kopecky and Zha ( 2020 ) state that decreases in deaths are either due to implementation of social distancing measures or to herd immunity; it is hard to identify and disentangle those factors using standard SIR models. They argue that with the “identification problem,” there will be considerable uncertainty about the conditions for restarting the economy. Only comprehensive testing can help resolve this ambiguity by quickly and accurately identifying new cases so that future outbreaks could be contained by isolation and contact‐tracing measures (Kopecky & Zha, 2020 ).

Agarwal et al. ( 2020 ) rely on synthetic control methods to investigate the effect of counterfactual mobility restriction interventions in United States. Using the daily death data from different countries, the authors create different “synthetic mobility United States” variables. These are applied to predict a counterfactual scenario and to understand the trade‐off between different levels of mobility interventions on death levels in United States. They find that a small decrease in mobility reduces the number of deaths; however, after registering a 40% drop in mobility, the benefits derived from mobility restrictions (in terms of the number of deaths) diminish. Using a counterfactual scenario, the authors find that lifting severe mobility restrictions but retaining moderate mobility restrictions (e.g., by imposing limitations in retail and public transport locations) might effectively reduce the number of deaths in United States. Others, such as Rampini ( 2020 ), make the case for the sequential lifting of lockdown measures for the younger population at the initial stages, followed by the older population at later stages. The authors state that the lower effect on the younger population group is a fortunate coincidence, and thus, the economic consequences of interventions can be greatly reduced by adopting a sequential approach. Oswald and Powdthavee ( 2020 ) make a similar case for releasing the younger population from mobility restrictions first on the grounds of higher economic efficiency (as they are more likely to be in the labor force) and their greater resilience against infections.

As some US states reopened, some researchers turned their focus on the immediate consequences. Nguyen et al. ( 2020 ) find that 4 days after reopening, mobility has increased by 6 to 8%, with greater increases across states which were late adopters of lockdown measures. These findings have important implications for the resurgence of cases, hospital capacity utilization, and further deaths. Dave, Friedson, Matsuzawa, and McNichols et al. ( 2020 ) analyze the effect of lifting the shelter‐in‐place order in Wisconsin, after the Wisconsin Supreme Court abolished it, on social distancing and the number of cases and find no statistically significant impact. W. Cheng et al. ( 2020 ) find that employment activity in the United States increased in May due to reopening in some states, mainly as a result of people who resumed working at their previous job. However, they find that the longer employees are separated from their firms, the more their re‐employment probabilities decline.

In regards to the aggregate macroeconomy, Gourinchas ( 2020 ) states that without substantial, timely, and stimulative macroeconomic intervention, the output lost from the economic downturn will be greatly amplified, especially as economic agents react to the negative shock by reducing consumption spending, investment spending, and engaging in lower credit transactions. The author suggests that there should be cross‐regional variation in government responses based on country characteristics. With high amounts of government debt and historically low interest levels existing in most developed countries, Bianchi et al. ( 2020 ) recommend a coordinated monetary and fiscal policy to address the COVID‐19 economic fallout. They recommend that fiscal policy should be used to enact an emergency budget with a ceiling placed on the debt‐to‐GDP ratio. This measure would increase aggregate spending, raise the inflation rate, and reduce real interest rates. The monetary authorities would need to coordinate with fiscal policy authorities by adopting an above‐normal inflation target. In the long run, governments would try to balance the budget, and future monetary policy would aim to bring inflation back to normal levels.

Bigio et al. ( 2020 ) focus on the cases for government transfers versus credit subsidy policies. They determine that the optimal mix between them depends on the level of financial development in the economy. According to these authors, economies with a developed financial system should utilize stimulative credit policies. On the other hand, developing economies should engage in more transfer spending. Guerrieri et al. ( 2020 ) show that the optimal economic policy response for the “Keynesian supply shock” induced by COVID‐19 would be to combine expansionary monetary policy and bolster social insurance programs for employees in the affected sectors. Unconventional policies, such as wage subsidies, helicopter drops of liquid assets, equity injections, and loan guarantees, can keep the economy in a full employment, high‐productivity equilibrium (Céspedes et al., 2020 ). These policies can break the cycle of negative feedback loops between corporate defaults and potential insolvency of financial intermediaries, which could culminate in a meltdown in financial markets (Elenev et al., 2020 ). Didier et al. ( 2020 ) discuss the policy menu, priorities, and trade‐offs of providing direct financing to firms.

Chetty et al. ( 2020 ) analyze the causal effect of policies implemented in the United States on households and businesses. They find that stimulus payments delivered through the Coronavirus Aid, Relief, and Economic Security (CARES) Act increased consumption spending, and that this spending was directed toward durable goods, which require low physical interaction at various stages of production. As a result, this spending is not directed toward small‐ and medium‐size businesses whose revenues were very adversely affected. On the other hand, loans to small businesses from the Paycheck Protection Program did little to restore employment among businesses. According to their analysis, the economic recovery depends on restoring consumer confidence and targeting income replacement programs rather than uniform lump‐sum stimulus payments.

Codagnone, Bogliacino, and Gómez, Folkvord et al. ( 2020 ) focus on the expectations of stakeholders with regards to the postlockdown period. Using an experimental survey in Spain, Italy, and United Kingdom, the authors find that exposure to the COVID‐19 shock and the ensuing lockdown led to pessimistic expectations about job opportunities, greater drawdowns of savings than before, and a deterioration in social relations which might be instrumental in finding job opportunities in the long run. The authors conclude that the fiscal policy measures might be insufficient in managing these expectations amidst uncertainties. They call on policy makers to draft contingency plans for exiting the lockdown—not only in terms of public expenditures earmarked for postlockdown operations, but also in terms of public health strategies to tackle a second wave of COVID‐19.

7. CONCLUSIONS

This study delved into the research related to the economics of COVID‐19 that has been released over a short time period. Our primary aim is to synthesize and to bring coherence and structure to the very rapidly growing body of relevant scientific evidence. By providing an annotated list of dozens of articles along with a brief capsule of their content, we hope to facilitate further research in the many strands of the COVID‐related literature. For readers who are interested in this critically important and pressing topic, this piece also provides an informative summary of the state of knowledge at the time of writing.

Before covering the impacts of COVID‐19, we documented the most popular data sources that are exploited to measure the known cases and deaths resulting from COVID‐19, as well as the social distancing activities. We first pointed out that the numbers of reported cases and deaths are subject to measurement error due to many factors, including testing capacity constraints and lags. Mobility measures that are based on GPS coordinates emitted from cell phones have been used extensively to measure social distancing. However, there are certain caveats that apply, particularly in terms of privacy concerns and the representativeness of data. The article also reviewed separate research related to social distancing activity itself, particularly in regards to its determinants, its efficacy in mitigating the spread of COVID‐19, and compliance with these orders. Going forward, social distancing actions and their measurements will continue to figure prominently in academic research and policy development.

We divided our coverage of the impact of the macroeconomy into two subsections, the first of which deals with the propagation mechanisms. The stay‐at‐home orders have very adverse effects on supply chains as well as on employment, which in turn causes drastic declines in consumption spending for many goods and services. The resultant declines in consumer and investor confidence reinforce negative multiplier effects in a downward spiral between labor and output markets, which can be partially attenuated by stimulative fiscal and monetary policies. Since the trajectory for the macroeconomy depends critically on the degree of spread of the virus itself, some researchers have integrated that element into their models. We reviewed the three potential “shapes” for the macroeconomic recovery: the highly optimistic yet implausible “V” path, the somewhat favorable “U” path, and the pessimistic yet more likely “L” path.

The second aspect of the macroeconomic impact of COVID‐19 that we discussed involves the forecasts. It is thought that the lockdown and social distancing measures wreak greater economic harm than the spread of the virus itself. The tremendous uncertainty regarding the path of the virus is compounded with a high degree of economic uncertainty such that these projections are subject to very wide confidence intervals and constant revisions. Some articles have attempted to address the longer‐term negative impacts on macro variables such as capital formation, productivity, and government finances. Other studies have focused on changes in patterns of consumption, employment, savings, and consumer debt by exploiting real‐time data.

In terms of the socioeconomic consequences of COVID‐19, we focus on the impact of the pandemic and the social distancing measures on outcomes in four areas: the labor market, mental health and well‐being, racial and gender inequality, and the environment. In terms of the labor market outcomes, research has shown that there is a high degree of heterogeneity in the pattern of job losses. The pandemic has caused a major shift toward work from home and away from positions involving F2F interactions with either the public or coworkers. Due to technological features and the nature of the services rendered, there are only a certain number of jobs that can be “feasibly” done from home and do not require F2F interactions. This contributes to the disproportionate effect of the pandemic on workers in certain industries and occupations, many of which have a relatively high concentration of lower‐skilled and/or less educated workers.

Social distancing measures have led to serious deteriorations in the levels of mental health, family stress, and domestic violence. Health care services for non‐COVID patients have been crowded out in many instances. There has been a marked rise in observed racial discrimination and sentiments of hostility toward certain ethnic groups. A growing number of studies also document that women have been adversely affected by the loss of child care and educational services for their children. The only seemingly positive consequence of social distancing/lockdown measures is the decrease in air pollution levels and the incidence of accidents involving motor vehicles. However, the impacts on the environment are multi‐faceted, and thus there remains a fair amount of ambiguity.

The goal of our piece was to survey and summarize the findings of the literature on the economics of COVID‐19. This was a very challenging task, as the literature is growing and evolving fast, and the pandemic is far from over at the time of writing. There are a few qualifications that are worth mentioning. First, very few of the research articles surveyed have undergone normal scientific review processes. Second, we mostly did not comment on methodology, which necessitates caution in interpretations. Finally, due to time as well as space constraints, we offer little in the way of critical analysis. Nonetheless, we hope this survey will facilitate further research in the many strands of the COVID‐related literature.

Supporting information

Table A: Major Pandemics: Historical Timeline

Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID‐19 . Journal of Economic Surveys . 2021; 35 :1007–1044. 10.1111/joes.12423 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 Social distancing (or physical distancing) is defined as maintaining physical space between yourself and other people residing outside one's home. To practice social/physical distancing: (i) stay at least 6 feet (about 2 arms’ lengths) from other people, (ii) do not gather in groups, and (iii) avoid crowded places and mass gatherings.

2 The list of NBER working article is available at this URL: https://www.nber.org/wp_covid19.html

3 The list of IZA discussion articles is available at this URL: https://covid‐19.iza.org/publications

4 See the link for the numbers and visual representation. Retrieved from https://coronavirus.jhu.edu

5 See WHO COVID‐19 Dashboard: https://covid19.who.int.

6 Refer to Johns Hopkins University ( 2020b ) for CFR data across countries.

7 See the link for further details: https://ourworldindata.org/mortality‐risk‐covid .

8 See the link for further details: https://covidtracking.com/data .

9 See the link for further details: https://covidtracking.com/race .

10 Mobility measures track work locations based on movements to a workplace from a reference point such as their home. However, if a person works from home or becomes unemployed, there will not be a distinct workplace reference point. Hence, the quality of mobility measures is expected to deteriorate.

11 The WHO Health System Response Monitor provides a cross country analysis and other details: https://analysis.covid19healthsystem.org/ .

12 According to the authors, if all members of a set choose to implement shelter‐in‐place policies, then the best response for agents is to follow. Hence, even in the absence of a federal mandate, the members of this “tipping set” can drive all others to adopt shelter‐in‐place policies.

13 The interaction between economic and epidemiological models is described in more details in the Online Appendix.

14 Further details on the “test and isolate” policy is available at the URL: https://paulromer.net/covid‐sim‐part1.

15 See the link for further details: https://www.google.com/covid19/mobility .

16 See the link for further details: https://www.unacast.com/covid19 .

17 See the link for further details: https://www.safegraph.com/dashboard/covid19‐commerce‐patterns .

18 See the link for further details: http://research.baidu.com/Blog/index‐view?id=13 .

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Peer-reviewed

Research Article

The impact of COVID-19 pandemic on physical and mental health of Asians: A study of seven middle-income countries in Asia

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Cognitive Neuroscience, Faculty of Education, Huaibei Normal University, Huaibei, China

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation College of Medicine, University of the Philippines, Manila, Philippines

Roles Conceptualization, Supervision, Visualization

Affiliation University Malaysia Sarawak (UNIMAS), Sarawak, Malaysia

Roles Conceptualization, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, Zahedan Branch, Islamic Azad University, Zahedan, Iran

Roles Conceptualization, Investigation, Methodology, Supervision, Visualization

Affiliation College of Public Health Sciences, Chulalongkorn University, a member of Thailand One Health University Network (THOHUN), Bangkok, Thailand

Roles Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Clinical Psychology, University of Karachi, Karachi, Pakistan

Affiliations Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America

Roles Formal analysis, Investigation, Methodology, Supervision, Validation

Affiliation DHQ Hospital Jhelum, Jhelum, Pakistan

Roles Formal analysis, Investigation, Methodology, Supervision

Affiliation Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam

Roles Investigation, Methodology, Project administration, Supervision, Validation

Affiliation Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam

Roles Data curation, Investigation, Methodology, Project administration, Supervision, Validation

Roles Investigation, Methodology, Supervision, Validation

Affiliation Faculty of Medicine, Duy Tan University, Da Nang, Vietnam

Roles Data curation, Investigation, Methodology, Supervision, Validation

Affiliation Department of Psychology, University of Sistan and Baluchestan, Zahedan, Iran

Affiliation Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

Roles Data curation, Project administration, Validation

Affiliation Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

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

Affiliation Mood Disorders Psychopharmacology Unit, University Health Network, University of Toronto, Toronto, Canada

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

Affiliation Department of Psychological Medicine, National University Health System, Singapore, Singapore

Roles Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Institute for Health Innovation and Technology (iHealthtech), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

•  [ ... ],

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation Southeast Asia One Health University Network (SEAOHUN), Chiang Mai, Thailand

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• Cuiyan Wang, 
• Michael Tee, 
• Ashley Edward Roy, 
• Mohammad A. Fardin, 
• Wandee Srichokchatchawan, 
• Hina A. Habib, 
• Bach X. Tran, 
• Shahzad Hussain, 
• Men T. Hoang, 

• Published: February 11, 2021
• https://doi.org/10.1371/journal.pone.0246824
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The coronavirus disease (COVID-19) pandemic has impacted the economy, livelihood, and physical and mental well-being of people worldwide. This study aimed to compare the mental health status during the pandemic in the general population of seven middle income countries (MICs) in Asia (China, Iran, Malaysia, Pakistan, Philippines, Thailand, and Vietnam). All the countries used the Impact of Event Scale–Revised (IES-R) and Depression, Anxiety and Stress Scale (DASS-21) to measure mental health. There were 4479 Asians completed the questionnaire with demographic characteristics, physical symptoms and health service utilization, contact history, knowledge and concern, precautionary measure, and rated their mental health with the IES-R and DASS-21. Descriptive statistics, One-Way analysis of variance (ANOVA), and linear regression were used to identify protective and risk factors associated with mental health parameters. There were significant differences in IES-R and DASS-21 scores between 7 MICs (p<0.05). Thailand had all the highest scores of IES-R, DASS-21 stress, anxiety, and depression scores whereas Vietnam had all the lowest scores. The risk factors for adverse mental health during the COVID-19 pandemic include age <30 years, high education background, single and separated status, discrimination by other countries and contact with people with COVID-19 (p<0.05). The protective factors for mental health include male gender, staying with children or more than 6 people in the same household, employment, confidence in doctors, high perceived likelihood of survival, and spending less time on health information (p<0.05). This comparative study among 7 MICs enhanced the understanding of metal health in the general population during the COVID-19 pandemic.

Citation: Wang C, Tee M, Roy AE, Fardin MA, Srichokchatchawan W, Habib HA, et al. (2021) The impact of COVID-19 pandemic on physical and mental health of Asians: A study of seven middle-income countries in Asia. PLoS ONE 16(2): e0246824. https://doi.org/10.1371/journal.pone.0246824

Editor: Tauqeer Hussain Mallhi, Jouf University, Kingdom of Saudi Arabia, SAUDI ARABIA

Received: October 17, 2020; Accepted: January 27, 2021; Published: February 11, 2021

Copyright: © 2021 Wang 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 paper and its Supporting Information files.

Funding: This study has the following funding sources: 1. Author C.W, 1 grant, Huaibei Normal University, China. 2. Author R.H, 1 grant, National University of Singapore iHealthtech Other Operating Expenses (R-722-000-004-731) 3. Author B.X.T, 1 grant, Vingroup Innovation Foundation (VINIF) COVID research grant (VINIF.2020.Covid19.DA07) in Vietnam

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

Introduction

Emerging psychiatric conditions and mental well-being were identified as the tenth most frequent research topic during the COVID-19 pandemic [ 1 ]. A recent systematic review found that relatively high rates of symptoms of anxiety, depression, post-traumatic stress disorder and stress were reported in the general population and health care professionals during the COVID-19 pandemic globally [ 2 , 3 ]. Asia has a number of middle income countries (MICs) that face tremendous economic challenges and limited medical resources to maintain physical and mental well-being during the pandemic [ 4 ]. This extended to North America as well, with the sudden change in economic security during COVID-19 projected to increase suicide rates [ 5 ]. During the pandemic, the Asia Pacific Disaster Mental Health Network recommended to establish a mental health agenda for Asia [ 6 ]. It is therefore important to conduct research to assess psychiatric status of Asians living in MICs to develop capacity of various health systems to respond to COVID-19. Previous studies mainly focused on mental health of individual Asian countries during the pandemic without cross comparison [ 7 – 9 ].

With no prior comparative study found on physical and mental health of Asians living in MICs during the COVID-19 pandemic, this study aimed to investigate the impact of the pandemic on physical and mental health in 7 Asian MICs (China, Iran, Malaysia, Pakistan, Philippines, Thailand and Vietnam), identify differences among countries, understand their concerns and precautions toward COVID-19, as well as to identify protective and risk factors associated with mental health outcomes.

Methodology

Study design and study population.

This was a cross-sectional study that involved seven countries. The recruitment was conducted after COVID-19 became an epidemic in each country. To minimize risks of COVID-19 infection, a respondent-driven sampling strategy on recruiting the general public was utilized where new participants were electronically invited by existing study respondents rather than face-to-face interaction. The respondents completed the questionnaires through an online survey platform (‘SurveyStar’, Changsha Ranxing Science and Technology in China, SurveyMonkey in Philippines, and Google Forms in other countries).

Ethics approval

The study was approved by the Institutional Review Boards from each MIC, China (Huaibei Normal University of China, HBU-IRB-2020-001/002), Iran (Islamic Azad University, Protocol Number: IRB-2020-001), Malaysia (Universiti Malaysia Sarawak, UNIMAS/NC-21.02/03-02 Jld.4 (85)), Pakistan (University of Karachi Protocol Number: ICP-1 (101) 2698), Philippines (University of Philippines Manila Research Ethics Board, UPMREB 2020-198-01), Thailand (Chulalongkorn University, COA No. 147/2563), and Vietnam (Hanoi Medical University, QD 75/QD-YHDP&YHDP). All IRBs allowed participants aged 12 years to 17 years to participate in this study and provide their own consent because the online survey did not pose any risk to research participants. All respondents provided informed consent. Confidentiality was maintained because no personally identifiable information was collected.

Measures and instruments

The COVID-19 online questionnaire designed by the National University of Singapore [ 10 ] had five sections: demographic, physical symptoms related to COVID-19 in the past 14 days, knowledge and concerns about COVID-19, precautionary measures against COVID, and views of health information required. Psychometric properties of the questionnaire were established in the initial phase and peak of the COVID-19 epidemic [ 8 , 9 ].

The psychological impact of COVID-19 was measured using the well-validated Impact of Event Scale-Revised (IES-R) in the Asians for determining the extent of psychological impact after exposure to a traumatic event (i.e., the COVID-19 pandemic) within one week of exposure [ 11 – 14 ]. In this study, the Cronbach’s alpha for different versions of IES-R is very high in all countries and ranges from 0.912–0.950. Cronbach’s alpha of 0.70 or higher in measuring the internal consistency is considered “acceptable” in most social science research [ 15 ].

The mental health status of respondents was measured using the Depression, Anxiety and Stress Scale (DASS-21) [ 16 ], which has been used to assess mental health in Asians [ 17 , 18 ]. Furthermore, DASS-21 assessed three domains (i.e. anxiety, depression and stress) and its psychometric properties was validated across clinical and non-clinical samples in different cultures and languages during the COVID-19 pandemic [ 19 ]. In this study, the Cronbach’s alpha (internal consistency) for different versions of DASS-21 is as follows: stress scale ranges from 0.839–0.934, anxiety scale ranges from 0.784–0.914, and depression scale ranges from 0.878–0.943. The IES-R and DASS-21 scales were previously used in research related to the COVID-19 epidemic [ 8 , 12 , 20 , 21 ]. The DASS and IES-R questionnaires are available in the public domain, and so permission is not required to use these two questionnaires [ 22 , 23 ].

Statistical analysis

Descriptive statistics were calculated to compare demographic characteristics, physical symptoms and health service utilization, contact history, knowledge and concern, precautionary measure and additional health information variables among 7 MICs. One-Way analysis of variance (ANOVA) was calculated to compare the mean IES-R and DASS-21 scores between 7 MICs in order to determine whether the associated population mean IES-R or DASS-21 scores were significantly different. If there were significant differences among 7 MICs, the Least Significant Difference (LSD) would calculate the smallest significant between mean scores of two countries with different combinations. Any difference larger than the LSD is considered a significant result. We used linear regressions to calculate the univariate associations between independent and dependent variables including the IES-S score and DASS-21 stress, anxiety and depression subscale scores for all respondents separately. All tests were two-tailed, with a significance level of p <0.05. Statistical analysis was performed on IBM SPSS Statistics version 21.0.

A total of 4479 participants from 7 MICs in Asia completed the survey. The distribution of the number of participants by country is listed as follows: China (27%), Philippines (19%), Malaysia (16.2%), Iran (12.3%), Thailand (11.6%), Pakistan (11.3%), and Vietnam (2.7%). Fig 1 compares the IES-R and DASS-21 scores amongst all 7 MICs in Asia.

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https://doi.org/10.1371/journal.pone.0246824.g001

The top three countries with highest IES-R scores were Thailand (mean 42.35, SD 13.39), China (mean 32.98, SD 15.42), and Iran (mean 30.42, SD 15.82). The top three countries with highest DASS-21 stress scores were Thailand (mean 21.94, SD 7.74), Pakistan (mean 14.02, SD 11.53) and Philippines (mean 10.60, SD 8.01). The top three countries with highest DASS-21 anxiety scores were Thailand (mean 18.66, SD 5.98), Pakistan (mean 8.23, SD 9.69) and Malaysia (mean 7.80, SD 10.95). The top three countries with highest DASS-21 depression scores were Thailand (mean 19.74, SD 6.99), Pakistan (mean 11.33, SD 11.28) and Philippines (mean 9.72, SD 8.99).

Differences in IES-R scores and DASS-21 stress, anxiety, depression scores amongst the 7 MICs were all statistically significant (IES-R: F(6, 4472) = 144.47, p<0.001, η2 = 0.16; Stress: F(6,4472) = 167.49 p<0.001, η2 = 0.18; Anxiety: F (6,4471) = 172.03, p<0.001, η2 = 0.19; Depression: F(6, 4472) = 137.11, p<0.001, η2 = 0.16). Vietnam had the lowest scores of IES-R (mean 17.39, SD 13.72), stress (mean 3.80, SD 5.81), anxiety (mean 2.10, SD 4.91) and depression (mean 2.28, SD 5.43). The LSD analysis revealed that the scores of Vietnam were significantly lower than the other countries (p<0.05).

S1 Table compares the demographics of 7 MICs. More than half of participants were women in all countries (Range: 52.6% in Pakistan to 76.8% in Thailand). More than half of Chinese, Filipino, Iranian and Pakistani participants were below age of 31 years. Majority of Chinese, Vietnamese and Malaysian respondents were married while majority of Filipino and Thai respondents were single. Majority of Filipino, Iranian, Pakistani, Malaysian and Thai respondents did not have children. More than half of participants stayed in a household with more than 3–5 people across all countries except Pakistan (49%). Majority of respondents from Philippines, Pakistan, Vietnam and Malaysia were employed when the study was conducted.

Table 1 shows the association between demographic characteristics of all participants and mental health parameters. Demographic characteristics associated with lower psychological impact were male gender whereas age younger than 30 years and students were associated with higher psychological impact. Participants who have children were associated with lower stress, anxiety and depression whereas participants with higher education, single and separated status were associated with higher stress, anxiety and depression. Staying with 6 or more people and those who were employed were associated with lower anxiety and depression.

https://doi.org/10.1371/journal.pone.0246824.t001

S2 Table shows the frequency of physical symptoms that resemble COVID-19 infection and there were significant differences among all countries. During the COVID-19 pandemic, the most common physical symptoms reported by general population in the 7 countries were headache (23.13%), cough (21.86%) and sore throat (19.29%). About 8.13% of respondents consulted General Practitioner (GP); 2.69% were hospitalized; 3.89% were tested positive for COVID-19 and 57.1% had a health insurance. Pakistani had the significantly highest proportion of respondents consulted GP (27.5%), hospitalized (16.4%), receiving COVID-19 test (17.2%) and being isolated (17.8%). Table 2 shows the association between physical symptoms and mental health outcomes. The physical symptoms that were significantly associated with higher scores in all mental health outcomes (IES-R and DASS-21 subscales) including rhinitis and persistent fever with cough or breathing difficulties. Chills or rigors, headache and nausea or vomiting were associated with higher DASS-21 stress and anxiety scores. Myalgia, cough, dizziness and sore throat were associated with higher score of IES-R. Usage of medical services such as seeing a doctor, hospitalization, recent COVID-19 testing, quarantine, poor rating of health status that were significantly associated with higher scores in all mental health outcomes (IES-R and DASS-21 subscales). History of chronic illness were significantly associated with higher DASS-21 subscale scores. Having medical insurance coverage was associated with higher IES-R scores.

https://doi.org/10.1371/journal.pone.0246824.t002

S3 Table shows the belief of route of transmission among participants in 7 MICs and there were significant differences among all countries. Out of all participants, there were a small number of participants who did not agree with transmission of COVID-19 being via droplets (10.34%) and contaminated objects (17.21%). It is interesting to note that China (60.5%) and Vietnam (59.8%) demonstrated significantly higher percentage of participants who believed in airborne transmission compared to .64.76% of participants from the other five countries who did not agree that COVID-19 was airborne transmitted.

Participants expressing confident and very confident in their doctors diagnosing COVID-19 were very high in Malaysia (93.8%) and China (92.9%); level of confidence was much lower in Iran (65.5%) and Pakistan (62.6%). About 50.26% of participants reported that they were likely and very likely to contract COVID-19, with Malaysian participants demonstrating the highest perceived risk of COVID-19 (72.8%) whilst the Filipino demonstrated the highest proportion of participants believing that they would not contract COVID-19 (53.2%). About 89.8% of Thai participants believed that they would survive if contracted with COVID-19 while the Pakistani had the highest proportion who believed that they would not survive COVID-19 (15.4%). About 78.43% of participants were satisfied with health information related to COVID-19; Vietnamese participants reported the highest proportion of satisfaction (97.5%). About 77.38% of participants were worried their family members contracting COVID-19. Pakistani participants reported the highest proportion of people who faced discrimination (42.7%). About 44.68% of participants spent more than 2 hours per day to view information on COVID-19 with Filipino participants having the highest proportion for spending more than 2 hours per day to view information (47.2%).

Table 3 shows the association between knowledge and concerns related to COVID-19 and mental health parameters. Agreement with airborne, contact with contaminated objects and droplet transmission was associated with higher DASS-21 in all subscales. Likelihood of contracting COVID-19, discrimination against by other countries and contact with people infected with COVID-19 were associated with higher IES-R or DASS-21 scores. Confidence in one’s own doctor diagnosing COVID-19, high likelihood of survival if infected with COVID-19 and spent less than two hours per day to monitor information relating to COVID-19 were associated with lower level of IES-R or DASS-21 scores.

https://doi.org/10.1371/journal.pone.0246824.t003

S4 Table shows the prevalence of precautionary measures and there were significant differences among 7 MICs (p<0.001). High percentages were reported by participants covering their mouth and nose after sneezing (98.0%), avoided sharing utensils (90.8%), practised hand hygiene (98.9%), washed hand after touching contaminated objects (96.2%), and wear face masks (93.5%). All Vietnamese participants (100%) responded wearing a face mask. About 68% of respondents felt that people were too worried about COVID-19 with Malaysia (90.5%), Thailand (90.5%) and Pakistan (86.6%) as the top three countries. Approximately 53% of respondents spent 20–24 hours per day at home; with China (84.7%), Iran (73.5%) and Philippines (55%) as the top three countries.

Table 4 shows the association between precautionary measures related to COVID-19 and mental health parameters. Avoidance of sharing cutlery dealing meals was associated with higher anxiety and depression. In contrast, hand hygiene practice was associated with lower IES-R and DASS-21 in all subscales. Wearing a face mask was associated with lower levels of stress and depression. Worries about COVID-19 was associated with significantly higher levels of DASS-21 in all subscales. Shorter duration of homestay was associated with higher levels of anxiety, depression and stress as compared to those who stayed at home for 20–24 hours per day.

https://doi.org/10.1371/journal.pone.0246824.t004

S5 Table compares the health information needs of participants from 7 MICs and there were significant differences among 7 MICs. The Chinese had the highest proportion who wanted to understand the symptoms of COVID-19 (91.6%), the prevention method (93.7%), effectiveness of drugs and vaccines (94.1%), number of infected cases and location (95.9%), travel advice (96.9%), mode of transmission (94.5%), required regular information update (92.7%) and personalized information (96.8%). The Iranians had the highest proportion who sought advices regarding treatment methods (90.4%) and Malaysians had the highest proportion who wanted to understand local outbreaks (94.2%).

Table 5 shows the association between health information needs about COVID-19 and mental health parameters. Most additional information including information on COVID-19 symptoms, prevention, treatment advice, needs for regular updates, knowledge on local transmission, effectiveness on drugs and vaccines, number of infected people based on geographical locations, travel advice and transmission mode of COVID were associated with higher IES-R scores. In contrast, the need for more personalized information, information on the effectiveness of drugs and vaccines, travel advices, transmission mode were associated with significantly lower level of depression.

https://doi.org/10.1371/journal.pone.0246824.t005

The main findings of this first multinational population-based study in MICs in Asia during the COVID-19 pandemic are summarized as follows. First, Thai respondents reported the highest levels of IES-R and DASS-21 scores. Second, Pakistani respondents reported the second highest levels of DASS-21 scores. Comparatively, Vietnamese respondents reported the lowest levels in DASS-21 scores. Third, Iranian respondents demonstrated the lowest confidence in their doctors whilst Pakistani respondents had the highest proportion who believed they would not survive COVID-19 and reported discrimination.

Assessing COVID-19’s association with respondents’ mental health, the three most common physical symptoms associated with adverse mental health were headache, cough and sore throat. Risk factors associated with adverse mental health during the COVID-19 pandemic include age <30 years old, high education background, single and separated status, discrimination by other countries, contact with people with COVID-19 and worries about COVID-19. Protective factors for mental health during the COVID-19 pandemic include male gender, staying with children, staying with 6 or more people, employment, confidence in own’s doctors diagnosing COVID-19, high perceived likelihood of surviving COVID-19, spending less time on health information, hand hygiene practice and wearing a face mask. Importantly, these findings will be significantly helpful for healthcare administrators in Asia at the national and local community levels [ 24 ] when preparing for the next wave of COVID-19 outbreak and future pandemics [ 25 ].

Iran had the highest total reported COVID cases (386,658) and number of COVID cases per 1 million people (4,593), as well as the highest number of deaths from COVID (22,293) and deaths per 1 million people (265) [ 26 ]. Pakistan had the second highest number of cases (298,509) and deaths (6,342) [ 26 ]. Of the 7 MICs, Vietnam had the lowest total numbers and rates across all seven countries, with 1,049 reported cases, 35 deaths and rates of just 11 cases and 0.4 deaths per 1 million [ 26 ]. As a result, Vietnamese respondents reported the lowest IES-R and DASS-21 scores. Vietnam has adopted several strategies to combat COVID-19 including development of the action plan and response strategies to optimize the utilization of human resources and equipment [ 24 ]; address the health information needs based on the diverse socioeconomic, demographic, and ethnic factors [ 27 ]; re-design communication activities for a more effective dissemination of information related to the epidemic [ 28 ]; safeguarding the health of workforce [ 29 ] to ensure minimal impact on economy and involvement of the grassroot system and village health collaborators to combat pandemics [ 30 , 31 ].

Thailand recorded the second lowest number of total cases (3,444) and deaths (58), and similarly the second lowest case rates (49) and death rates (0.8) per 1 million [ 26 ]. Surprisingly, we found that Thailand was the country with the highest IES-R and DASS-21 depression scores. This could be due to the impact of COVID-19 on the economy in Thailand. Among all MICs in Asia, the disruption on COVID-19 pandemic is the most severe on Thailand economy, due to its reliance on tourism as compared to other MICs. For 2020, the International Monetary Fund has predicted Thailand’s GDP to be reduced by 6.7 percent which is highest among Asian countries [ 32 ]. Pakistan ranked second in terms of DASS-21 scores and number of COVID cases and deaths. The congruence between psychological parameters and epidemiology of COVID-19 in Pakistan was due to poor sanitation, lack of basic preventive measures, lack of proper testing and medical facilities. Pakistani health professionals started protesting and threatened to quit work due to lack of Personal Protective Equipment (PPE) [ 33 ]. Currently, the vaccination coverage in rural Pakistan remains unsatisfactory amid various barriers including price, hesitancy, and low level of awareness [ 34 ]. Eid-ul-Adha is an annual religious festival that could not be cancelled due to religious obligations and led to a sharp spike in COVID-19 cases [ 35 ]. The unpreparedness and contradictory policies resulted in an alarming high rate of COVID-19 spread and worsening mental health and discrimination faced by Pakistani people. Iranian respondents demonstrated lowest confidence in their doctors. The economic sanctions that prevented medical supplies, equipment and drugs from arriving in Iran could lead to low confidence among Iranians [ 36 ].

This study highlighted unique protective factors for mental health in MICs of Asia. In this study, more than 90% of respondents agreed to wear masks to prevent COVID-19. During the initial stage of COVID-19 pandemic, medical and public health experts from the US and some European countries believed that there was no direct evidence of airborne transmission of COVID-19 [ 37 ]. In contrast, respiratory clinicians and public health experts from Asia argued that lack of evidence does not equate to evidence of ineffectiveness of face masks [ 38 ]. The use of face masks by Asians have played an important role in controlling the spread of COVID-19 [ 39 ]. This study showed the association between the use of face mask and lower DASS-21 anxiety and depression scores. This finding might support the postulation that wearing face mask could offer psychological benefits, such as feeling less vulnerable to infection via perceived control [ 37 ]. Staying with children and more than 6 people in the same household were protective factors due to the values of family support among Asians. Compared with western countries, family support has a greater influence on reducing the risk of adverse mental health in Asia [ 10 ].

The findings of this first multinational study have several implications for health and government policies. Firstly, the health authorities should offer psychological interventions to the general population who are at higher risk of developing adverse mental health including women, people younger than 30 years and single and separated status. High education background is a risk factor and online psychological interventions such as cognitive behaviour therapy (CBT) and mindfulness-based therapy could improve mental health for highly educated individual [ 40 ]. For countries with high IES-R scores (Thailand, China and Iran), online trauma-focused CBT that promotes trauma narration, problem solving related to problems associated with COVID-19 and home based relaxation could be helpful in reducing psychological impact [ 9 ]. Second, as physical symptoms resembling COVID-19 infection (e.g., rhinitis, persistent fever with cough, breathing difficulties) were associated with high IES-R and DASS-21 scores groups. There is an urgent need to develop accurate, rapid diagnostic tests in general practitioners’ clinics, community and rural settings [ 31 ]. A negative COVID-19 test result may alleviate anxiety, depression, stress and psychological impact. Enhancing the capacity of health system to combat COVID-19 may increase the confidence of public and improve mental health. Third, based on our findings, the WHO, governments and health authorities should provide regular updates on the effectiveness of vaccines and treatment methods. Mis-information related to the cause of COVID-19 [ 41 ], rumours [ 42 ] and inconsistent information [ 43 ] on COVID-19 symptoms, prevention, treatment and transmission mode were associated with negative psychological impact. Local governments, news agencies, professional and advocacy organisations should all provide health information and advices related to COVID-19 that are consistent with national guidelines and avoid mis-information [ 44 ]. It is important to identify group-specific demands would be helpful to provide proper information related to COVID-19 to fulfil the need of different population groups [ 27 ]. Various governments should offer relief packages to safeguard employment and economy to protect mental health. Additionally, the level of policy stringency in response to COVID-19 or pandemics, as measured by the Oxford Stringency Index, may influence mental health and should be moderated accordingly by respective governments [ 45 ].

This study has several limitations. First, the findings of this study were based on seven MICs in Asia and could not be generated to other countries. The study population had different sociodemographic characteristics as compared to the general population in the world due to sampling bias because only participants with Internet access could participate in this online survey. The respondent sampling method also compromised the representativeness of samples. The study population was female predominant (proportion of female in the study population: 67.76%; world population 49.58%) [ 46 ] and a high proportion of the study population possessed a university degree (85.6%). Thus, there is a potential risk of sampling bias because we could not reach out to potential respondents without Internet access. The second limitation was the cross-sectional nature of this study and inability to demonstrate cause and effect relationship. The third limitation was that we did not record demographic data regarding pre-existing mental illness of the study participants. The fourth limitation is that self-reported levels of psychological impact, anxiety, depression and stress may not always be aligned with objective assessment by mental health professionals. Nevertheless, psychological impact, anxiety, depression and stress are based on personal feelings, and self-reporting was paramount during the COVID-19 pandemic. The fifth limitation is that we did not study other aspects of the pandemic such as the potential threat of self-medication of hydroxychloroquine and cholorquine [ 47 ] and precautionary measures of walkthrough sanitization gates [ 48 ]. Lastly, we were unable to calculate the response rate. For potential respondents who were not keen to participate in the online survey, no response was recorded, and we could not collect any information from them.

Conclusions

In conclusion, this multi-national study across 7 MICs in Asia showed that Thai reported the highest mean IES-R and DASS-21 anxiety, depression and stress scores. In contrast, Vietnamese reported the lowest mean scores in IES-R and DASS-21 anxiety, depression and stress scales. The risk factors for adverse mental health include age < 30 years, high education background, single and separated status, discrimination by other countries, contact with people with COVID-19 and worries about COVID-19. The protective factors for mental health include male gender, staying with children, staying with 6 or more people, employment, confidence in own’s doctors diagnosing COVID-19, high perceived likelihood of surviving COVID-19, spending less time on health information, hand hygiene practice and wearing a face mask.

Supporting information

S1 table. comparison of demographics of the participants from seven countries..

https://doi.org/10.1371/journal.pone.0246824.s001

S2 Table. Physical symptoms resembling COVID-19 infection reported by the participants from seven countries.

https://doi.org/10.1371/journal.pone.0246824.s002

S3 Table. Comparison of knowledge related to COVID-19 in participants of the seven countries.

https://doi.org/10.1371/journal.pone.0246824.s003

S4 Table. Comparison of precautionary measures related to COVID-19 in the participants of the seven countries.

https://doi.org/10.1371/journal.pone.0246824.s004

S5 Table. Comparison of information needs about COVID-19 in the participants of the seven Asian countries.

https://doi.org/10.1371/journal.pone.0246824.s005

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Why the Pandemic Probably Started in a Lab, in 5 Key Points

By Alina Chan

Dr. Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “Viral: The Search for the Origin of Covid-19.”

This article has been updated to reflect news developments.

On Monday, Dr. Anthony Fauci returned to the halls of Congress and testified before the House subcommittee investigating the Covid-19 pandemic. He was questioned about several topics related to the government’s handling of Covid-19, including how the National Institute of Allergy and Infectious Diseases, which he directed until retiring in 2022, supported risky virus work at a Chinese institute whose research may have caused the pandemic.

For more than four years, reflexive partisan politics have derailed the search for the truth about a catastrophe that has touched us all. It has been estimated that at least 25 million people around the world have died because of Covid-19, with over a million of those deaths in the United States.

Although how the pandemic started has been hotly debated, a growing volume of evidence — gleaned from public records released under the Freedom of Information Act, digital sleuthing through online databases, scientific papers analyzing the virus and its spread, and leaks from within the U.S. government — suggests that the pandemic most likely occurred because a virus escaped from a research lab in Wuhan, China. If so, it would be the most costly accident in the history of science.

Here’s what we now know:

1 The SARS-like virus that caused the pandemic emerged in Wuhan, the city where the world’s foremost research lab for SARS-like viruses is located.

• At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengli.
• Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live r oughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

The closest known relatives to SARS-CoV-2 were found in southwestern China and in Laos.

Large cities

Mine in Yunnan province

Cave in Laos

South China Sea

The closest known relatives to SARS-CoV-2

were found in southwestern China and in Laos.

philippines

The closest known relatives to SARS-CoV-2 were found

in southwestern China and Laos.

Sources: Sarah Temmam et al., Nature; SimpleMaps

Note: Cities shown have a population of at least 200,000.

There are hundreds of large cities in China and Southeast Asia.

There are hundreds of large cities in China

and Southeast Asia.

The pandemic started roughly 1,000 miles away, in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away,

in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away, in Wuhan,

home to the world’s foremost SARS-like virus research lab.

• Even at hot spots where these viruses exist naturally near the cave bats of southwestern China and Southeast Asia, the scientists argued, as recently as 2019 , that bat coronavirus spillover into humans is rare .
• When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory , saying she had never expected such an outbreak to occur in Wuhan.
• The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire . Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

2 The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature.

• Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals , as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.
• The laboratory pursued risky research that resulted in viruses becoming more infectious : Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.
• By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started , when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.
• In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse , which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.
• Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. T he Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic. Defuse was never funded by the United States . However, in his testimony on Monday, Dr. Fauci explained that the Wuhan institute would not need to rely on U.S. funding to pursue research independently.

The Wuhan lab ran risky experiments to learn about how SARS-like viruses might infect humans.

1. Collect SARS-like viruses from bats and other wild animals, as well as from people exposed to them.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of human cells.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of

human cells.

In Defuse, the scientists proposed to add a furin cleavage site to the spike protein.

3. Create new coronaviruses by inserting spike proteins or other features that could make the viruses more infectious in humans.

4. Infect human cells, civets and humanized mice with the new coronaviruses, to determine how dangerous they might be.

• While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.
• Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.
• When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.
• Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse. In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak, and initiated proceedings to bar them from receiving future grants. In his testimony on Monday, Dr. Fauci said that he supported the decision to suspend and bar EcoHealth.
• Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him . Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.
• In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military . In the congressional hearing on Monday, Dr. Fauci repeatedly acknowledged the lack of visibility into experiments conducted at the Wuhan institute, saying, “None of us can know everything that’s going on in China, or in Wuhan, or what have you. And that’s the reason why — I say today, and I’ve said at the T.I.,” referring to his transcribed interview with the subcommittee, “I keep an open mind as to what the origin is.”

3 The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

• Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions .

In the United States, virologists generally use stricter Biosafety Level 3 protocols when working with SARS-like viruses.

Biosafety cabinets prevent

viral particles from escaping.

Viral particles

Personal respirators provide

a second layer of defense against breathing in the virus.

DIRECT CONTACT

Gloves prevent skin contact.

Disposable wraparound

gowns cover much of the rest of the body.

Personal respirators provide a second layer of defense against breathing in the virus.

Disposable wraparound gowns

cover much of the rest of the body.

Note: ​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

The Wuhan lab had been regularly working with SARS-like viruses under Biosafety Level 2 conditions, which could not prevent a highly infectious virus like SARS-CoV-2 from escaping.

Some work is done in the open air, and masks are not required.

Less protective equipment provides more opportunities

for contamination.

Some work is done in the open air,

and masks are not required.

Less protective equipment provides more opportunities for contamination.

• In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice . Wuhan institute scientists handled these live viruses at low biosafet y levels , including BSL-2.
• Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping . Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels , and the risks are much greater for scientists working with infectious pathogens at low biosafety.
• An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.” Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak : “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”
• SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.
• One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019 . One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick .

4 The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

• In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed. To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.
• A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019. More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.
• Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

An analysis of SARS-CoV-2’s evolutionary tree shows how the virus evolved as it started to spread through humans.

SARS-COV-2 Viruses closest

to bat coronaviruses

more mutations

Source: Lv et al., Virus Evolution (2024) , as reproduced by Jesse Bloom

The viruses that infected people linked to the market were most likely not the earliest form of the virus that started the pandemic.

• Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

5 Key evidence that would be expected if the virus had emerged from the wildlife trade is still missing.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals.

Infected animals

Earliest known

cases exposed to

live animals

Antibody evidence

of animals and

animal traders having

been infected

Ancestral variants

of the virus found in

Documented trade

of host animals

between the area

where bats carry

closely related viruses

and the outbreak site

Infected animals found

Earliest known cases exposed to live animals

Antibody evidence of animals and animal

traders having been infected

Ancestral variants of the virus found in animals

Documented trade of host animals

between the area where bats carry closely

related viruses and the outbreak site

For SARS-CoV-2, these same key pieces of evidence are still missing , more than four years after the virus emerged.

For SARS-CoV-2, these same key pieces of evidence are still missing ,

more than four years after the virus emerged.

• Despite the intense search trained on the animal trade and people linked to the market, investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.
• Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade . For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill. No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.
• With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species . Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections. In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals . The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

The pandemic could have been caused by any of hundreds of virus species, at any of tens of thousands of wildlife markets, in any of thousands of cities, and in any year. But it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

While several natural spillover scenarios remain plausible, and we still don’t know enough about the full extent of virus research conducted at the Wuhan institute by Dr. Shi’s team and other researchers, a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

Whether the pandemic started on a lab bench or in a market stall, it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them . Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics .

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic. Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

A thorough investigation by the U.S. government could unearth more evidence while spurring whistleblowers to find their courage and seek their moment of opportunity. It would also show the world that U.S. leaders and scientists are not afraid of what the truth behind the pandemic may be.

More on how the pandemic may have started

Where Did the Coronavirus Come From? What We Already Know Is Troubling.

Even if the coronavirus did not emerge from a lab, the groundwork for a potential disaster had been laid for years, and learning its lessons is essential to preventing others.

By Zeynep Tufekci

Why Does Bad Science on Covid’s Origin Get Hyped?

If the raccoon dog was a smoking gun, it fired blanks.

By David Wallace-Wells

A Plea for Making Virus Research Safer

A way forward for lab safety.

By Jesse Bloom

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Alina Chan ( @ayjchan ) is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “ Viral : The Search for the Origin of Covid-19.” She was a member of the Pathogens Project , which the Bulletin of the Atomic Scientists organized to generate new thinking on responsible, high-risk pathogen research.

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