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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.

World Health Organization. Timeline - COVID-19: Available at: https://www.who.int/news/item/29-06-2020-covidtimeline . Accessed 1 June 2021.

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available at: https://coronavirus.jhu.edu/map.html . Accessed 1 June 2021.

Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, et al. Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19). J Clin Med. 2020;9(2):601.

Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395–400. https://doi.org/10.1126/science.aba9757 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fidahic M, Nujic D, Runjic R, Civljak M, Markotic F, Lovric Makaric Z, et al. Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19. BMC Med Res Methodol. 2020;20(1):161. https://doi.org/10.1186/s12874-020-01047-2 .

EPPI Centre . COVID-19: a living systematic map of the evidence. Available at: http://eppi.ioe.ac.uk/cms/Projects/DepartmentofHealthandSocialCare/Publishedreviews/COVID-19Livingsystematicmapoftheevidence/tabid/3765/Default.aspx . Accessed 1 June 2021.

NCBI SARS-CoV-2 Resources. Available at: https://www.ncbi.nlm.nih.gov/sars-cov-2/ . Accessed 1 June 2021.

Gustot T. Quality and reproducibility during the COVID-19 pandemic. JHEP Rep. 2020;2(4):100141. https://doi.org/10.1016/j.jhepr.2020.100141 .

Article   PubMed   PubMed Central   Google Scholar  

Kodvanj, I., et al., Publishing of COVID-19 Preprints in Peer-reviewed Journals, Preprinting Trends, Public Discussion and Quality Issues. Preprint article. bioRxiv 2020.11.23.394577; doi: https://doi.org/10.1101/2020.11.23.394577 .

Dobler CC. Poor quality research and clinical practice during COVID-19. Breathe (Sheff). 2020;16(2):200112. https://doi.org/10.1183/20734735.0112-2020 .

Article   Google Scholar  

Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 .

Lunny C, Brennan SE, McDonald S, McKenzie JE. Toward a comprehensive evidence map of overview of systematic review methods: paper 1-purpose, eligibility, search and data extraction. Syst Rev. 2017;6(1):231. https://doi.org/10.1186/s13643-017-0617-1 .

Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of Reviews. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.1 (updated September 2020). Cochrane. 2020. Available from www.training.cochrane.org/handbook .

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions version 6.1 (updated September 2020). Cochrane. 2020; Available from www.training.cochrane.org/handbook .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):18. https://doi.org/10.1186/s13643-018-0914-3 .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. A decision tool to help researchers make decisions about including systematic reviews in overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):29. https://doi.org/10.1186/s13643-018-0768-8 .

Hunt H, Pollock A, Campbell P, Estcourt L, Brunton G. An introduction to overviews of reviews: planning a relevant research question and objective for an overview. Syst Rev. 2018;7(1):39. https://doi.org/10.1186/s13643-018-0695-8 .

Pollock M, Fernandes RM, Pieper D, Tricco AC, Gates M, Gates A, et al. Preferred reporting items for overviews of reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):335. https://doi.org/10.1186/s13643-019-1252-9 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med. 2009;3(3):e123–30.

Krnic Martinic M, Pieper D, Glatt A, Puljak L. Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol. 2019;19(1):203. https://doi.org/10.1186/s12874-019-0855-0 .

Puljak L. If there is only one author or only one database was searched, a study should not be called a systematic review. J Clin Epidemiol. 2017;91:4–5. https://doi.org/10.1016/j.jclinepi.2017.08.002 .

Article   PubMed   Google Scholar  

Gates M, Gates A, Guitard S, Pollock M, Hartling L. Guidance for overviews of reviews continues to accumulate, but important challenges remain: a scoping review. Syst Rev. 2020;9(1):254. https://doi.org/10.1186/s13643-020-01509-0 .

Covidence - systematic review software. Available at: https://www.covidence.org/ . Accessed 1 June 2021.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Borges do Nascimento IJ, et al. Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis. J Clin Med. 2020;9(4):941.

Article   PubMed Central   Google Scholar  

Adhikari SP, Meng S, Wu YJ, Mao YP, Ye RX, Wang QZ, et al. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty. 2020;9(1):29. https://doi.org/10.1186/s40249-020-00646-x .

Cortegiani A, Ingoglia G, Ippolito M, Giarratano A, Einav S. A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. J Crit Care. 2020;57:279–83. https://doi.org/10.1016/j.jcrc.2020.03.005 .

Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol. 2020;109(5):531–8. https://doi.org/10.1007/s00392-020-01626-9 .

Article   CAS   PubMed   Google Scholar  

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577–83. https://doi.org/10.1002/jmv.25757 .

Lippi G, Lavie CJ, Sanchis-Gomar F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): evidence from a meta-analysis. Prog Cardiovasc Dis. 2020;63(3):390–1. https://doi.org/10.1016/j.pcad.2020.03.001 .

Lippi G, Henry BM. Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19). Eur J Intern Med. 2020;75:107–8. https://doi.org/10.1016/j.ejim.2020.03.014 .

Lippi G, Plebani M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chim Acta. 2020;505:190–1. https://doi.org/10.1016/j.cca.2020.03.004 .

Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta. 2020;506:145–8. https://doi.org/10.1016/j.cca.2020.03.022 .

Ludvigsson JF. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 2020;109(6):1088–95. https://doi.org/10.1111/apa.15270 .

Lupia T, Scabini S, Mornese Pinna S, di Perri G, de Rosa FG, Corcione S. 2019 novel coronavirus (2019-nCoV) outbreak: a new challenge. J Glob Antimicrob Resist. 2020;21:22–7. https://doi.org/10.1016/j.jgar.2020.02.021 .

Marasinghe, K.M., A systematic review investigating the effectiveness of face mask use in limiting the spread of COVID-19 among medically not diagnosed individuals: shedding light on current recommendations provided to individuals not medically diagnosed with COVID-19. Research Square. Preprint article. doi : https://doi.org/10.21203/rs.3.rs-16701/v1 . 2020 .

Mullins E, Evans D, Viner RM, O’Brien P, Morris E. Coronavirus in pregnancy and delivery: rapid review. Ultrasound Obstet Gynecol. 2020;55(5):586–92. https://doi.org/10.1002/uog.22014 .

Pang J, Wang MX, Ang IYH, Tan SHX, Lewis RF, Chen JIP, et al. Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel coronavirus (2019-nCoV): a systematic review. J Clin Med. 2020;9(3):623.

Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623. https://doi.org/10.1016/j.tmaid.2020.101623 .

Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. AJR Am J Roentgenol. 2020;215(1):87–93. https://doi.org/10.2214/AJR.20.23034 .

Sun P, Qie S, Liu Z, Ren J, Li K, Xi J. Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis. J Med Virol. 2020;92(6):612–7. https://doi.org/10.1002/jmv.25735 .

Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–5. https://doi.org/10.1016/j.ijid.2020.03.017 .

Bassetti M, Vena A, Giacobbe DR. The novel Chinese coronavirus (2019-nCoV) infections: challenges for fighting the storm. Eur J Clin Investig. 2020;50(3):e13209. https://doi.org/10.1111/eci.13209 .

Article   CAS   Google Scholar  

Hwang CS. Olfactory neuropathy in severe acute respiratory syndrome: report of a case. Acta Neurol Taiwanica. 2006;15(1):26–8.

Google Scholar  

Suzuki M, Saito K, Min WP, Vladau C, Toida K, Itoh H, et al. Identification of viruses in patients with postviral olfactory dysfunction. Laryngoscope. 2007;117(2):272–7. https://doi.org/10.1097/01.mlg.0000249922.37381.1e .

Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many estimates of the COVID-19 case fatality rate. Lancet Infect Dis. 2020;20(7):776–7. https://doi.org/10.1016/S1473-3099(20)30244-9 .

Wolkewitz M, Puljak L. Methodological challenges of analysing COVID-19 data during the pandemic. BMC Med Res Methodol. 2020;20(1):81. https://doi.org/10.1186/s12874-020-00972-6 .

Rombey T, Lochner V, Puljak L, Könsgen N, Mathes T, Pieper D. Epidemiology and reporting characteristics of non-Cochrane updates of systematic reviews: a cross-sectional study. Res Synth Methods. 2020;11(3):471–83. https://doi.org/10.1002/jrsm.1409 .

Runjic E, Rombey T, Pieper D, Puljak L. Half of systematic reviews about pain registered in PROSPERO were not published and the majority had inaccurate status. J Clin Epidemiol. 2019;116:114–21. https://doi.org/10.1016/j.jclinepi.2019.08.010 .

Runjic E, Behmen D, Pieper D, Mathes T, Tricco AC, Moher D, et al. Following Cochrane review protocols to completion 10 years later: a retrospective cohort study and author survey. J Clin Epidemiol. 2019;111:41–8. https://doi.org/10.1016/j.jclinepi.2019.03.006 .

Tricco AC, Antony J, Zarin W, Strifler L, Ghassemi M, Ivory J, et al. A scoping review of rapid review methods. BMC Med. 2015;13(1):224. https://doi.org/10.1186/s12916-015-0465-6 .

COVID-19 Rapid Reviews: Cochrane’s response so far. Available at: https://training.cochrane.org/resource/covid-19-rapid-reviews-cochrane-response-so-far . Accessed 1 June 2021.

Cochrane. Living systematic reviews. Available at: https://community.cochrane.org/review-production/production-resources/living-systematic-reviews . Accessed 1 June 2021.

Millard T, Synnot A, Elliott J, Green S, McDonald S, Turner T. Feasibility and acceptability of living systematic reviews: results from a mixed-methods evaluation. Syst Rev. 2019;8(1):325. https://doi.org/10.1186/s13643-019-1248-5 .

Babic A, Poklepovic Pericic T, Pieper D, Puljak L. How to decide whether a systematic review is stable and not in need of updating: analysis of Cochrane reviews. Res Synth Methods. 2020;11(6):884–90. https://doi.org/10.1002/jrsm.1451 .

Lovato A, Rossettini G, de Filippis C. Sore throat in COVID-19: comment on “clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis”. J Med Virol. 2020;92(7):714–5. https://doi.org/10.1002/jmv.25815 .

Leung C. Comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1431–2. https://doi.org/10.1002/jmv.25912 .

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. Response to Char’s comment: comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1433. https://doi.org/10.1002/jmv.25924 .

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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

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

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Thilo Caspar von Groote

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Livia Puljak

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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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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

Johns Hopkins Coronavirus Resource Center. Covid-19 Case Tracker. Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html (2020).

US Food and Drug Administration. Coronavirus (COVID-19) update: FDA issues emergency use authorization for potential COVID-19 treatment. FDA News Release . https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-issues-emergency-use-authorization-potential-covid-19-treatment (2020).

Evans, S. R. Fundamentals of clinical trial design. J. Exp. Stroke Transl. Med. 3 , 19–27 (2010).

Article   Google Scholar  

Antman, E. M. & Bierer, B. E. Standards for clinical research: keeping pace with the technology of the future. Circulation 133 , 823–825 (2016).

Food and Drug Administration. FDA guidance on conduct of clinical trials of medical products during COVID-19 public health emergency. Guidance for Industry, Investigators and Institutional Review Boards . https://www.fda.gov/regulatory-information/search-fda-guidance-documents/fda-guidance-conduct-clinical-trials-medical-products-during-covid-19-public-health-emergency (2020).

National Institutes of Health. NIH clinical trials shows remdesivir accelerates recovery from advanced COVID-19. NIH New Releases . https://www.nih.gov/news-events/news-releases/nih-clinical-trial-shows-remdesivir-accelerates-recovery-advanced-covid-19#.XrIX75ZmQeQ.email (2020).

Radcliffe, S. Here’s exactly where we are with vaccines and treatments for COVID-19. Health News . https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-for-covid-19 (2020).

Abbasi, J. The promise and peril of antibody testing for COVID-19. JAMA . https://doi.org/10.1001/jama.2020.6170 (2020).

CDC COVID-19 Response Team. Coronavirus disease 2019 in children—United States, February 12–April 2, 2020. Morb. Mortal Wkly Rep . 69 , 422–426 (2020).

Agarwal, N. Opinion: the coronavirus could cause a child abuse epidemic. The New York Times . https://www.nytimes.com/2020/04/07/opinion/coronavirus-child-abuse.html (2020).

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

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Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Greater Good Science Center • Magazine • In Action • In Education

11 Questions to Ask About COVID-19 Research

Debates have raged on social media, around dinner tables, on TV, and in Congress about the science of COVID-19. Is it really worse than the flu? How necessary are lockdowns? Do masks work to prevent infection? What kinds of masks work best? Is the new vaccine safe?

You might see friends, relatives, and coworkers offer competing answers, often brandishing studies or citing individual doctors and scientists to support their positions. With so much disagreement—and with such high stakes—how can we use science to make the best decisions?

Here at Greater Good , we cover research into social and emotional well-being, and we try to help people apply findings to their personal and professional lives. We are well aware that our business is a tricky one.

Summarizing scientific studies and distilling the key insights that people can apply to their lives isn’t just difficult for the obvious reasons, like understanding and then explaining formal science terms or rigorous empirical and analytic methods to non-specialists. It’s also the case that context gets lost when we translate findings into stories, tips, and tools, especially when we push it all through the nuance-squashing machine of the Internet. Many people rarely read past the headlines, which intrinsically aim to be relatable and provoke interest in as many people as possible. Because our articles can never be as comprehensive as the original studies, they almost always omit some crucial caveats, such as limitations acknowledged by the researchers. To get those, you need access to the studies themselves.

And it’s very common for findings and scientists to seem to contradict each other. For example, there were many contradictory findings and recommendations about the use of masks, especially at the beginning of the pandemic—though as we’ll discuss, it’s important to understand that a scientific consensus did emerge.

Given the complexities and ambiguities of the scientific endeavor, is it possible for a non-scientist to strike a balance between wholesale dismissal and uncritical belief? Are there red flags to look for when you read about a study on a site like Greater Good or hear about one on a Fox News program? If you do read an original source study, how should you, as a non-scientist, gauge its credibility?

Here are 11 questions you might ask when you read about the latest scientific findings about the pandemic, based on our own work here at Greater Good.

1. Did the study appear in a peer-reviewed journal?

In peer review, submitted articles are sent to other experts for detailed critical input that often must be addressed in a revision prior to being accepted and published. This remains one of the best ways we have for ascertaining the rigor of the study and rationale for its conclusions. Many scientists describe peer review as a truly humbling crucible. If a study didn’t go through this process, for whatever reason, it should be taken with a much bigger grain of salt. 

“When thinking about the coronavirus studies, it is important to note that things were happening so fast that in the beginning people were releasing non-peer reviewed, observational studies,” says Dr. Leif Hass, a family medicine doctor and hospitalist at Sutter Health’s Alta Bates Summit Medical Center in Oakland, California. “This is what we typically do as hypothesis-generating but given the crisis, we started acting on them.”

In a confusing, time-pressed, fluid situation like the one COVID-19 presented, people without medical training have often been forced to simply defer to expertise in making individual and collective decisions, turning to culturally vetted institutions like the Centers for Disease Control (CDC). Is that wise? Read on.

2. Who conducted the study, and where did it appear?

“I try to listen to the opinion of people who are deep in the field being addressed and assess their response to the study at hand,” says Hass. “With the MRNA coronavirus vaccines, I heard Paul Offit from UPenn at a UCSF Grand Rounds talk about it. He literally wrote the book on vaccines. He reviewed what we know and gave the vaccine a big thumbs up. I was sold.”

From a scientific perspective, individual expertise and accomplishment matters—but so does institutional affiliation.

Why? Because institutions provide a framework for individual accountability as well as safety guidelines. At UC Berkeley, for example , research involving human subjects during COVID-19 must submit a Human Subjects Proposal Supplement Form , and follow a standard protocol and rigorous guidelines . Is this process perfect? No. It’s run by humans and humans are imperfect. However, the conclusions are far more reliable than opinions offered by someone’s favorite YouTuber .

Recommendations coming from institutions like the CDC should not be accepted uncritically. At the same time, however, all of us—including individuals sporting a “Ph.D.” or “M.D.” after their names—must be humble in the face of them. The CDC represents a formidable concentration of scientific talent and knowledge that dwarfs the perspective of any one individual. In a crisis like COVID-19, we need to defer to that expertise, at least conditionally.

“If we look at social media, things could look frightening,” says Hass. When hundreds of millions of people are vaccinated, millions of them will be afflicted anyway, in the course of life, by conditions like strokes, anaphylaxis, and Bell’s palsy. “We have to have faith that people collecting the data will let us know if we are seeing those things above the baseline rate.”

3. Who was studied, and where?

Animal experiments tell scientists a lot, but their applicability to our daily human lives will be limited. Similarly, if researchers only studied men, the conclusions might not be relevant to women, and vice versa.

Many psychology studies rely on WEIRD (Western, educated, industrialized, rich and democratic) participants, mainly college students, which creates an in-built bias in the discipline’s conclusions. Historically, biomedical studies also bias toward gathering measures from white male study participants, which again, limits generalizability of findings. Does that mean you should dismiss Western science? Of course not. It’s just the equivalent of a “Caution,” “Yield,” or “Roadwork Ahead” sign on the road to understanding.

This applies to the coronavirus vaccines now being distributed and administered around the world. The vaccines will have side effects; all medicines do. Those side effects will be worse for some people than others, depending on their genetic inheritance, medical status, age, upbringing, current living conditions, and other factors.

For Hass, it amounts to this question: Will those side effects be worse, on balance, than COVID-19, for most people?

“When I hear that four in 100,000 [of people in the vaccine trials] had Bell’s palsy, I know that it would have been a heck of a lot worse if 100,000 people had COVID. Three hundred people would have died and many others been stuck with chronic health problems.”

4. How big was the sample?

In general, the more participants in a study, the more valid its results. That said, a large sample is sometimes impossible or even undesirable for certain kinds of studies. During COVID-19, limited time has constrained the sample sizes.

However, that acknowledged, it’s still the case that some studies have been much larger than others—and the sample sizes of the vaccine trials can still provide us with enough information to make informed decisions. Doctors and nurses on the front lines of COVID-19—who are now the very first people being injected with the vaccine—think in terms of “biological plausibility,” as Hass says.

Did the admittedly rushed FDA approval of the Pfizer-BioNTech vaccine make sense, given what we already know? Tens of thousands of doctors who have been grappling with COVID-19 are voting with their arms, in effect volunteering to be a sample for their patients. If they didn’t think the vaccine was safe, you can bet they’d resist it. When the vaccine becomes available to ordinary people, we’ll know a lot more about its effects than we do today, thanks to health care providers paving the way.

5. Did the researchers control for key differences, and do those differences apply to you?

Diversity or gender balance aren’t necessarily virtues in experimental research, though ideally a study sample is as representative of the overall population as possible. However, many studies use intentionally homogenous groups, because this allows the researchers to limit the number of different factors that might affect the result.

While good researchers try to compare apples to apples, and control for as many differences as possible in their analyses, running a study always involves trade-offs between what can be accomplished as a function of study design, and how generalizable the findings can be.

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You also need to ask if the specific population studied even applies to you. For example, when one study found that cloth masks didn’t work in “high-risk situations,” it was sometimes used as evidence against mask mandates.

However, a look beyond the headlines revealed that the study was of health care workers treating COVID-19 patients, which is a vastly more dangerous situation than, say, going to the grocery store. Doctors who must intubate patients can end up being splattered with saliva. In that circumstance, one cloth mask won’t cut it. They also need an N95, a face shield, two layers of gloves, and two layers of gown. For the rest of us in ordinary life, masks do greatly reduce community spread, if as many people as possible are wearing them.

6. Was there a control group?

One of the first things to look for in methodology is whether the population tested was randomly selected, whether there was a control group, and whether people were randomly assigned to either group without knowing which one they were in. This is especially important if a study aims to suggest that a certain experience or treatment might actually cause a specific outcome, rather than just reporting a correlation between two variables (see next point).

For example, were some people randomly assigned a specific meditation practice while others engaged in a comparable activity or exercise? If the sample is large enough, randomized trials can produce solid conclusions. But, sometimes, a study will not have a control group because it’s ethically impossible. We can’t, for example, let sick people go untreated just to see what would happen. Biomedical research often makes use of standard “treatment as usual” or placebos in control groups. They also follow careful ethical guidelines to protect patients from both maltreatment and being deprived necessary treatment. When you’re reading about studies of masks, social distancing, and treatments during the COVID-19, you can partially gauge the reliability and validity of the study by first checking if it had a control group. If it didn’t, the findings should be taken as preliminary.

7. Did the researchers establish causality, correlation, dependence, or some other kind of relationship?

We often hear “Correlation is not causation” shouted as a kind of battle cry, to try to discredit a study. But correlation—the degree to which two or more measurements seem connected—is important, and can be a step toward eventually finding causation—that is, establishing a change in one variable directly triggers a change in another. Until then, however, there is no way to ascertain the direction of a correlational relationship (does A change B, or does B change A), or to eliminate the possibility that a third, unmeasured factor is behind the pattern of both variables without further analysis.

In the end, the important thing is to accurately identify the relationship. This has been crucial in understanding steps to counter the spread of COVID-19 like shelter-in-place orders. Just showing that greater compliance with shelter-in-place mandates was associated with lower hospitalization rates is not as conclusive as showing that one community that enacted shelter-in-place mandates had lower hospitalization rates than a different community of similar size and population density that elected not to do so.

We are not the first people to face an infection without understanding the relationships between factors that would lead to more of it. During the bubonic plague, cities would order rodents killed to control infection. They were onto something: Fleas that lived on rodents were indeed responsible. But then human cases would skyrocket.

Why? Because the fleas would migrate off the rodent corpses onto humans, which would worsen infection. Rodent control only reduces bubonic plague if it’s done proactively; once the outbreak starts, killing rats can actually make it worse. Similarly, we can’t jump to conclusions during the COVID-19 pandemic when we see correlations.

8. Are journalists and politicians, or even scientists, overstating the result?

Language that suggests a fact is “proven” by one study or which promotes one solution for all people is most likely overstating the case. Sweeping generalizations of any kind often indicate a lack of humility that should be a red flag to readers. A study may very well “suggest” a certain conclusion but it rarely, if ever, “proves” it.

This is why we use a lot of cautious, hedging language in Greater Good , like “might” or “implies.” This applies to COVID-19 as well. In fact, this understanding could save your life.

When President Trump touted the advantages of hydroxychloroquine as a way to prevent and treat COVID-19, he was dramatically overstating the results of one observational study. Later studies with control groups showed that it did not work—and, in fact, it didn’t work as a preventative for President Trump and others in the White House who contracted COVID-19. Most survived that outbreak, but hydroxychloroquine was not one of the treatments that saved their lives. This example demonstrates how misleading and even harmful overstated results can be, in a global pandemic.

9. Is there any conflict of interest suggested by the funding or the researchers’ affiliations?

A 2015 study found that you could drink lots of sugary beverages without fear of getting fat, as long as you exercised. The funder? Coca Cola, which eagerly promoted the results. This doesn’t mean the results are wrong. But it does suggest you should seek a second opinion : Has anyone else studied the effects of sugary drinks on obesity? What did they find?

It’s possible to take this insight too far. Conspiracy theorists have suggested that “Big Pharma” invented COVID-19 for the purpose of selling vaccines. Thus, we should not trust their own trials showing that the vaccine is safe and effective.

But, in addition to the fact that there is no compelling investigative evidence that pharmaceutical companies created the virus, we need to bear in mind that their trials didn’t unfold in a vacuum. Clinical trials were rigorously monitored and independently reviewed by third-party entities like the World Health Organization and government organizations around the world, like the FDA in the United States.

Does that completely eliminate any risk? Absolutely not. It does mean, however, that conflicts of interest are being very closely monitored by many, many expert eyes. This greatly reduces the probability and potential corruptive influence of conflicts of interest.

10. Do the authors reference preceding findings and original sources?

The scientific method is based on iterative progress, and grounded in coordinating discoveries over time. Researchers study what others have done and use prior findings to guide their own study approaches; every study builds on generations of precedent, and every scientist expects their own discoveries to be usurped by more sophisticated future work. In the study you are reading, do the researchers adequately describe and acknowledge earlier findings, or other key contributions from other fields or disciplines that inform aspects of the research, or the way that they interpret their results?

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This was crucial for the debates that have raged around mask mandates and social distancing. We already knew quite a bit about the efficacy of both in preventing infections, informed by centuries of practical experience and research.

When COVID-19 hit American shores, researchers and doctors did not question the necessity of masks in clinical settings. Here’s what we didn’t know: What kinds of masks would work best for the general public, who should wear them, when should we wear them, were there enough masks to go around, and could we get enough people to adopt best mask practices to make a difference in the specific context of COVID-19 ?

Over time, after a period of confusion and contradictory evidence, those questions have been answered . The very few studies that have suggested masks don’t work in stopping COVID-19 have almost all failed to account for other work on preventing the disease, and had results that simply didn’t hold up. Some were even retracted .

So, when someone shares a coronavirus study with you, it’s important to check the date. The implications of studies published early in the pandemic might be more limited and less conclusive than those published later, because the later studies could lean on and learn from previously published work. Which leads us to the next question you should ask in hearing about coronavirus research…

11. Do researchers, journalists, and politicians acknowledge limitations and entertain alternative explanations?

Is the study focused on only one side of the story or one interpretation of the data? Has it failed to consider or refute alternative explanations? Do they demonstrate awareness of which questions are answered and which aren’t by their methods? Do the journalists and politicians communicating the study know and understand these limitations?

When the Annals of Internal Medicine published a Danish study last month on the efficacy of cloth masks, some suggested that it showed masks “make no difference” against COVID-19.

The study was a good one by the standards spelled out in this article. The researchers and the journal were both credible, the study was randomized and controlled, and the sample size (4,862 people) was fairly large. Even better, the scientists went out of their way to acknowledge the limits of their work: “Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others.”

Unfortunately, their scientific integrity was not reflected in the ways the study was used by some journalists, politicians, and people on social media. The study did not show that masks were useless. What it did show—and what it was designed to find out—was how much protection masks offered to the wearer under the conditions at the time in Denmark. In fact, the amount of protection for the wearer was not large, but that’s not the whole picture: We don’t wear masks mainly to protect ourselves, but to protect others from infection. Public-health recommendations have stressed that everyone needs to wear a mask to slow the spread of infection.

“We get vaccinated for the greater good, not just to protect ourselves ”

As the authors write in the paper, we need to look to other research to understand the context for their narrow results. In an editorial accompanying the paper in Annals of Internal Medicine , the editors argue that the results, together with existing data in support of masks, “should motivate widespread mask wearing to protect our communities and thereby ourselves.”

Something similar can be said of the new vaccine. “We get vaccinated for the greater good, not just to protect ourselves,” says Hass. “Being vaccinated prevents other people from getting sick. We get vaccinated for the more vulnerable in our community in addition for ourselves.”

Ultimately, the approach we should take to all new studies is a curious but skeptical one. We should take it all seriously and we should take it all with a grain of salt. You can judge a study against your experience, but you need to remember that your experience creates bias. You should try to cultivate humility, doubt, and patience. You might not always succeed; when you fail, try to admit fault and forgive yourself.

Above all, we need to try to remember that science is a process, and that conclusions always raise more questions for us to answer. That doesn’t mean we never have answers; we do. As the pandemic rages and the scientific process unfolds, we as individuals need to make the best decisions we can, with the information we have.

This article was revised and updated from a piece published by Greater Good in 2015, “ 10 Questions to Ask About Scientific Studies .”

About the Authors

Jeremy Adam Smith

Uc berkeley.

Jeremy Adam Smith edits the GGSC’s online magazine, Greater Good . He is also the author or coeditor of five books, including The Daddy Shift , Are We Born Racist? , and (most recently) The Gratitude Project: How the Science of Thankfulness Can Rewire Our Brains for Resilience, Optimism, and the Greater Good . Before joining the GGSC, Jeremy was a John S. Knight Journalism Fellow at Stanford University.

Emiliana R. Simon-Thomas

Emiliana R. Simon-Thomas, Ph.D. , is the science director of the Greater Good Science Center, where she directs the GGSC’s research fellowship program and serves as a co-instructor of its Science of Happiness and Science of Happiness at Work online courses.

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A Deep Look into the Biology and Evolution of COVID-19

Uctv roundtable explores the biological roots and spread of the global sars-cov-2 virus.

Of the hundreds of coronaviruses known to exist, many are relatively harmless. Coronaviruses infect your nose, sinuses and upper throat but often result in nothing more than a common cold (see Know Your Coronaviruses ).

So what makes the new SARS-CoV-2, the virus that has caused a global pandemic, such a society-altering threat?

Probing the biological basis of the novel virus and evolutionary spread of the COVID-19 disease it causes, a panel of UC San Diego biologists gathered for a special roundtable analysis hosted by UCTV. The program is available here: A Deep Look into the Biology and Evolution of COVID-19.

Roundtable moderator Suresh Subramani, distinguished professor emeritus in the Division of Biological Sciences and director of the Tata Institute for Genetics and Society, framed the program by highlighting three major areas of concern surrounding the pandemic and how it impacts our daily lives: the rapid spread of the virus over the past three months; the ominous morbidity and mortality rates of the disease, which threaten to overwhelm global health care systems; and the immense reservoir of carriers of the disease.

“It is estimated that there may be tenfold more asymptomatic carriers of the disease, which means that there could be over seven-and-a-half million carriers worldwide,” said Subramani. “This is a disease that is spreading very rapidly across the globe, so these faculty are here to share their knowledge regarding the biology of the virus and why this pandemic has brought the world to its knees.”

Emily Troemel, a professor who studies host-pathogen interactions in the Section of Cell and Developmental Biology, kicked off the discussion by describing basic biological aspects of coronaviruses, including how health workers test for the presence of SARS-CoV-2 infection and facets scientists have learned about the virus’ genome.

Coronaviruses, as Troemel noted, feature RNA-based genomes, unlike most of life on the planet, which feature DNA genomes. RNA genomes in coronaviruses are positive-sense, which are similar to the cell’s own messenger RNA and allows these viruses to immediately hijack the protein synthesis machinery of host cells. This feature enables these viruses to quickly and effectively take over host cells and rapidly expand.

“Knowing that it has RNA in its genome helps us understand how we test for the presence of coronavirus,” said Troemel. “In addition, we are able to look at changes in the sequence in the viral genome and that’s enabling us to track the spread of this virus around the globe…. We can learn about how the biology of the virus is changing and how it may be altering the way it interacts with host cells, and also potentially different ways that we could treat it. It’s part of an amazing open science effort with an unprecedented level of information acquisition and information sharing among researchers.”

Credit: iStock.com/ChakisAtelier

Matt Daugherty, an assistant professor in the Section of Molecular Biology, studies the evolutionary arms race that pits the immune systems of hosts on one hand and pathogens on the other. He covered aspects such as how SARS-CoV-2 and other viruses enter the human population and become pandemics; how SARS-CoV-2 relates to past and present epidemic viruses in the human population; and, based on what scientists have learned from other viruses, what we can expect in terms of long-term immunity and co-existence with SARS-CoV-2.

“We as a species are always being exposed to viruses,” Daugherty noted.

Since SARS-CoV-2 is so new, there are many key unknowns related to human immune defenses against it, Daugherty said. Even with coronaviruses that cause common colds, it’s unclear whether humans develop long-term immunity to these viruses or need to continually develop new immunities.

“One thing I take comfort in with all of these other viruses is knowing that we aren’t constantly dealing with influenza pandemics and other pandemic viruses, and that’s because of the largely effective role of our immune system in dealing with these viruses once the immune system has been prepared,” said Daugherty.

For a virus that originated in an animal species to successfully infect humans, it needs to adapt to a range of genetic differences between the original host species and humans. But effective vaccines can ultimately thwart such pathogens.

“We have really good ways of making effective vaccines, and the hope is that this will hold for SARS-CoV-2 as well,” said Daugherty. “I take some comfort in knowing that these types of pandemics do pass and we will get through this.”

Justin Meyer, an assistant professor in the Section of Ecology, Behavior and Evolution, discussed concepts related to science and society’s ability to predict future pandemics. These include variables that contribute to the spread of pathogens; the increased likelihood of future pandemics; and predictions for where the next pandemic is likely to occur.

Factors that boost the risk of pandemics include human exposure to pathogens through meat consumption and contact with wild animals, increased human encroachment in wild areas and the exotic animal trade. Increased urbanization—more people living in close proximity means more opportunities for viruses to spread—and the rising consequences of climate change, also increase pandemic risks.

“We’re augmenting the temperature of the earth and environments in a way that we’re making ourselves more susceptible to diseases,” said Meyer. “When we warm the earth, we create more habitats for mosquitoes that carry vectors like malaria by increasing their range. They can spread to new human populations..... By increasing temperatures, we’re increasing flooding and there are many pathogens that are waterborne, such as cholera, which we will be exposing more and more people to.”

During the roundtable discussion, Subramani prompted the scientists with a handful of questions, including: Since many coronaviruses are relatively harmless, what makes SARS-CoV-2 so damaging to the lungs? What is the appropriate vaccine target for SARS-CoV-2 and in what time frame—from validation to FDA approval—is a vaccine likely? Can we look to drug targets where vaccines have been developed for related viruses and would that timeline be the same? Is there any evidence that SARS-CoV-2 has a mutation rate that is extraordinarily high?

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• 1 College of Medicine, University of the Philippines Manila, Manila, Philippines
• 2 Faculty of Education, Institute of Cognitive Neuroscience, Huaibei Normal University, Huaibei, China
• 3 Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
• 4 Southeast Asia One Health University Network, Chiang Mai, Thailand
• 5 Department of Psychological Medicine, National University Health System, Singapore, Singapore
• 6 Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Singapore

Objective: The differences between the physical and mental health of people living in a lower-middle-income country (LMIC) and upper-middle-income country (UMIC) during the COVID-19 pandemic was unknown. This study aimed to compare the levels of psychological impact and mental health between people from the Philippines (LMIC) and China (UMIC) and correlate mental health parameters with variables relating to physical symptoms and knowledge about COVID-19.

Methods: The survey collected information on demographic data, physical symptoms, contact history, and knowledge about COVID-19. The psychological impact was assessed using the Impact of Event Scale-Revised (IES-R), and mental health status was assessed by the Depression, Anxiety, and Stress Scale (DASS-21).

Findings: The study population included 849 participants from 71 cities in the Philippines and 861 participants from 159 cities in China. Filipino (LMIC) respondents reported significantly higher levels of depression, anxiety, and stress than Chinese (UMIC) during the COVID-19 ( p < 0.01) while only Chinese respondents' IES-R scores were above the cut-off for PTSD symptoms. Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection ( p < 0.05), recent use of but with lower confidence on medical services ( p < 0.01), recent direct and indirect contact with COVID ( p < 0.01), concerns about family members contracting COVID-19 ( p < 0.001), dissatisfaction with health information ( p < 0.001). In contrast, Chinese respondents requested more health information about COVID-19. For the Philippines, student status, low confidence in doctors, dissatisfaction with health information, long daily duration spent on health information, worries about family members contracting COVID-19, ostracization, and unnecessary worries about COVID-19 were associated with adverse mental health. Physical symptoms and poor self-rated health were associated with adverse mental health in both countries ( p < 0.05).

Conclusion: The findings of this study suggest the need for widely available COVID-19 testing in MIC to alleviate the adverse mental health in people who present with symptoms. A health education and literacy campaign is required in the Philippines to enhance the satisfaction of health information.

Introduction

The World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) to be a Public Health Emergency of International Concern on January 30 ( 1 ) and a pandemic on March 11, 2020 ( 2 ). COVID-19 predominantly presents with respiratory symptoms (cough, sneezing, and sore throat), along with fever, fatigue and myalgia. It is thought to spread through droplets, contaminated surfaces, and asymptomatic individuals ( 3 ). By the end of April, over 3 million people have been infected globally ( 4 ).

The first country to identify the novel virus as the cause of the pandemic was China. The authorities responded with unprecedented restrictions on movement. The response included stopping public transport before Chinese New Year, an annual event that sees workers' mass emigration to their hometowns, and a lockdown of whole cities and regions ( 1 ). Two new hospitals specifically designed for COVID-19 patients were rapidly built in Wuhan. Such measures help slow the transmission of COVID-19 in China. As of May 2, there are 83,959 confirmed cases and 4,637 deaths from the virus in China ( 4 ). The Philippines was also affected early by the current crisis. The first case was suspected on January 22, and the country reported the first death from COVID-19 outside of mainland China ( 5 ). Similar to China, the Philippines implemented lockdowns in Manila. Other measures included the closure of schools and allowing arrests for non-compliance with measures ( 6 ). At the beginning of May, the Philippines recorded 8,772 cases and 579 deaths ( 4 ).

China was one of the more severely affected countries in Asia in the early stage of pandemic ( 7 ) while the Philippines is still experiencing an upward trend in the COVID-19 cases ( 6 ). The gross national income (GNI) per capita of the Philippines and China are USD 3,830 and 9,460, respectively, were classified with lower (LMIC) and upper-middle-income countries (UMIC) by the Worldbank ( 8 ). During the COVID-19 pandemic, five high-income countries (HIC), including the United States, Italy, the United Kingdom, Spain, and France, account for 70% of global deaths ( 9 ). The HIC faced the following challenges: (1) the lack of personal protection equipment (PPE) for healthcare workers; (2) the delay in response strategy; (3) an overstretched healthcare system with the shortage of hospital beds, and (4) a large number of death cases from nursing homes ( 10 ). The COVID-19 crisis threatens to hit lower and middle-income countries due to lockdown excessively and economic recession ( 11 ). A systematic review on mental health in LMIC in Asia and Africa found that LMIC: (1) do not have enough mental health professionals; (2) the negative economic impact led to an exacerbation of mental issues; (3) there was a scarcity of COVID-19 related mental health research in Asian LMIC ( 12 ). This systematic review could not compare participants from different middle-income countries because each study used different questionnaires. During the previous Severe Acute Respiratory Syndrome (SARS) epidemic, the promotion of protective personal health practices to reduce transmission of the SARS virus was found to reduce the anxiety levels in the community ( 13 ).

Before COVID-19, previous studies found that stress might be a modifiable risk factor for depression in LMICs ( 14 ) and UMICs ( 15 – 17 ). Another study involving thirty countries found that unmodifiable risk factors for depression included female gender, and depression became more common in 2004 to 2014 compared to previous periods ( 18 ). Further, there were cultural differences in terms of patient-doctor relationship and attitudes toward healthcare systems before the COVID-19 pandemic. In China, <20% of the general public and medical professionals view the doctor and patient relationship as harmonious ( 19 ). In contrast, Filipino seemed to have more trust and be compliant to doctors' recommendations ( 20 ). Patient satisfaction was more important than hospital quality improvement to maintain patient loyalty to the Chinese healthcare system ( 21 ). For Filipinos, improvement in the quality of healthcare service was found to improve patients' satisfaction ( 22 ).

Based on the above studies, we have the following research questions: (1) whether COVID-19 pandemic could be an important stressor and risk factor for depression for the people living in LMIC and UMIC ( 23 ), (2) Are physical symptoms that resemble COVID-19 infection and other concerns be risk factors for adverse mental health? (3) Are knowledge of COVID-19 and health information protective factors for mental health? (4) Would there be any cultural differences in attitudes toward doctors and healthcare systems during the pandemic between China and the Philippines? We hypothesized that UMIC (China) would have better physical and mental health than LMIC (the Philippines). The aims of this study were (a) to compare the physical and mental health between citizens from an LMIC (the Philippines) and UMIC (China); (b) to correlate psychological impact, depression, anxiety, and stress scores with variables relating to physical symptoms, knowledge, and concerns about COVID-19 in people living in the Philippines (LMIC) and China (UMIC).

Study Design and Study Population

We conducted a cross-cultural and quantitative study to compare Filipinos' physical and mental health with Chinese during the COVID-19 pandemic. The study was conducted from February 28 to March 1 in China and March 28 to April 7, 2020 in the Philippines, when the number of COVID-19 daily reported cases increased in both countries. The Chinese participants were recruited from 159 cities and 27 provinces. The Filipino participants, on the other hand, were recruited from 71 cities and 40 provinces representing the Luzon, Visayas, and Mindanao archipelago. A respondent-driven recruitment strategy was utilized in both countries. The recruitment started with a set of initial respondents who were associated with the Huaibei Normal University of China and the University of the Philippines Manila; who referred other participants by email and social network; these in turn refer other participants across different cities in China and the Philippines.

As both Chinese and Filipino governments recommended that the public minimize face-to-face interaction and isolate themselves during the study period, new respondents were electronically invited by existing study respondents. The respondents completed the questionnaires through an online survey platform (“SurveyStar,” Changsha Ranxing Science and Technology in China and Survey Monkey Online Survey in the Philippines). The Institutional Review Board of the University of Philippines Manila Research Ethics Board (UPMREB 2020-198-01) and Huaibei Normal University (China) approved the research proposal (HBU-IRB-2020-002). All respondents provided informed or implied consent. The collected data were anonymous and treated as confidential.

This study used the National University of Singapore COVID-19 questionnaire, and its psychometric properties had been established in the initial phase of the COVID-19 epidemic ( 24 ). The National University of Singapore COVID-19 questionnaire consisted of questions that covered several areas: (1) demographic data; (2) physical symptoms related to COVID-19 in the past 14 days; (3) contact history with COVID-19 in the past 14 days; and (4) knowledge and concerns about COVID-19.

Demographic data about age, gender, education, household size, marital status, parental status, and residential city in the past 14 days were collected. Physical symptoms related to COVID-19 included breathing difficulty, chills, coryza, cough, dizziness, fever, headache, myalgia, sore throat, nausea, vomiting, and diarrhea. Respondents also rated their physical health status and stated their history of chronic medical illness. In the past 14 days, health service utilization variables included consultation with a doctor in the clinic, being quarantined by the health authority, recent testing for COVID-19 and medical insurance coverage. Knowledge and concerns related to COVID-19 included knowledge about the routes of transmission, level of confidence in diagnosis, source, and level of satisfaction of health information about COVID-19, the likelihood of contracting and surviving COVID-19 and the number of hours spent on viewing information about COVID-19 per day.

The psychological impact of COVID-19 was measured using the Impact of Event Scale-Revised (IES-R). The IES-R is a self-administered questionnaire that has been well-validated in the European and Asian population for determining the extent of psychological impact after exposure to a traumatic event (i.e., the COVID-19 pandemic) within one week of exposure ( 25 , 26 ). This 22-item questionnaire, composed of three subscales, aims to measure the mean avoidance, intrusion, and hyperarousal ( 27 ). The total IES-R score is divided into 0–23 (normal), 24–32 (mild psychological impact), 33–36 (moderate psychological impact) and >37 (severe psychological impact) ( 28 ). The total IES-R score > 24 suggests the presence of post-traumatic stress disorder (PTSD) symptoms ( 29 ).

The respondents' mental health status was measured using the Depression, Anxiety, and Stress Scale (DASS-21) and the calculation of scores was based on a previous Asian study ( 30 ). DASS has been demonstrated to be a reliable and valid measure in assessing mental health in Filipinos ( 31 – 33 ) and Chinese ( 34 , 35 ). IES-R and DASS-21 were previously used in research related to the COVID-19 epidemic ( 26 , 36 – 38 ).

Statistical Analysis

Descriptive statistics were calculated for demographic characteristics, physical symptom, and health service utilization variables, contact history variables, knowledge and concern variables, precautionary measure variables, and additional health information variables. To analyze the differences in the levels of psychological impact, levels of depression, anxiety and stress, the independent sample t -test was used to compare the mean score between the Filipino (LMIC) and Chinese (UMIC) respondents. The chi-squared test was used to analyze the differences in categorical variables between the two samples. We used linear regressions to calculate the univariate associations between independent and dependent variables, including the IES-S score and DASS stress, anxiety, and depression subscale scores for the Filipino and Chinese respondents separately with adjustment for age, marital status, and education levels. All tests were two-tailed, with a significance level of p < 0.05. Statistical analysis was performed on SPSS Statistic 21.0.

Demographic Characteristics and Their Association With Psychological Impact and Adverse Mental Health Status

We received 849 responses from the Philippines and 861 responses from China for 1,710 individual respondents from both countries. The majority of Filipino respondents were women (71.0%), age between 22 and 30 years (26.6%), having a household size of 3–5 people (53.4%), high educational attainment (91.4% with a bachelor or higher degree), and married (68.9%). Similarly, the majority of Chinese respondents were women (75%), having a household size of 3–5 people (80.4%) and high educational attainment (91.4% with a bachelor or higher degree). There was a significantly higher proportion of Chinese respondents who had children younger than 16 years ( p < 0.001) and student status ( p < 0.001; See Table 1 ).

Table 1 . Comparison of demographic characteristics between Filipino (LMIC) and Chinese (UMIC) respondents ( N = 1,710).

For Filipino respondents, the male gender and having a child were protective factors significantly associated with the lower score of IES-R ( p < 0.05) and depression ( p < 0.001), respectively. Single status was significantly associated with depression ( p < 0.05), and student status was associated with higher IES-R, stress and depression scores ( p < 0.01) (see Table 2 ). For Chinese respondents, the male gender was significantly associated with a lower score of IES-R but higher DASS depression scores ( p < 0.01). Notwithstanding, there were other differences between Filipino and China respondents. Chinese respondents who stayed in a household with 3–5 people ( p < 0.05) and more than 6 people ( p < 0.05) were significantly associated with a higher score of IES-R as compared to respondents who stayed alone.

Table 2 . Comparison of the association between demographic variables and the psychological impact as well as adverse mental health status between Filipino (LMIC) and Chinese (UMIC) respondents ( n = 1,710).

Comparison Between the Filipino (LMIC) and Chinese (UMIC) Respondents and Their Mental Health Status

Figure 1 compares the mean scores of DASS-stress, anxiety, and depression subscales and IES-R scores between the Filipino and Chinese respondents. For the DASS-stress subscale, Filipino respondents reported significantly higher stress ( p < 0.001), anxiety ( p < 0.01), and depression ( p < 0.01) than Chinese (UMIC). For IES-R, Filipino (LMIC) had significantly lower scores than Chinese ( p < 0.001). The mean IES-R scores of Chinese were higher than 24 points, indicating the presence of PTSD symptoms in Chinese respondents only.

Figure 1 . Comparison of the mean scores of DASS-stress, anxiety and depression subscales, and IES-R scores between Filipino and Chinese respondents.

Physical Symptoms, Health Status, and Its Association With Psychological Impact and Adverse Mental Health Status

There were significant differences between Filipino (LMIC) and Chinese (UMIC) respondents regarding physical symptoms resembling COVID-19 and health status. There was a significantly higher proportion of Filipino respondents who reported headache ( p < 0.001), myalgia ( p < 0.001), cough ( p < 0.001), breathing difficulty ( p < 0.001), dizziness ( p < 0.05), coryza ( p < 0.001), sore throat ( p < 0.001), nausea and vomiting ( p < 0.001), recent consultation with a doctor ( p < 0.01), recent hospitalization ( p < 0.001), chronic illness ( p < 0.001), direct ( p < 0.001), and indirect ( p < 0.001) contact with a confirmed diagnosis of COVID-19 as compared to Chinese (see Supplementary Table 1 ). Significantly more Chinese respondents were under quarantine ( p < 0.001).

Linear regression showed that headache, myalgia, cough, dizziness, coryza as well as poor self-rated physical health were significantly associated with higher IES-R scores, DASS-21 stress, anxiety, and depression subscale scores in both countries after adjustment for confounding factors ( p < 0.05; see Table 3 ). Furthermore, breathing difficulty, sore throat, and gastrointestinal symptoms were significantly associated with higher DASS-21 stress, anxiety and depression subscale scores in both countries ( p < 0.05). Chills were significantly associated with higher DASS-21 stress and depression scores ( p < 0.01) in both countries. Recent quarantine was associated with higher DASS-21 subscale scores in Chinese respondents only ( p < 0.05).

Table 3 . Association between physical health status and contact history and the perceived impact of COVID-19 outbreak as well as adverse mental health status during the epidemic after adjustment for age, gender, and marital status ( n = 1,710).

Perception, Knowledge, and Concerns About COVID-19 and Its Association With Psychological Impact and Adverse Mental Health Status

Filipino (LMIC) and Chinese (UMIC) respondents held significantly different perceptions in terms of knowledge and concerns related to COVID-19 (see Supplementary Table 2 ). For the routes of transmission, there were significantly more Filipino respondents who agreed that droplets transmitted the COVID-19 ( p < 0.001) and contact via contaminated objects ( p < 0.001), but significantly more Chinese agreed with the airborne transmission ( p < 0.001). For the detection and risk of contracting COVID-19, there were significantly more Filipino who were not confident about their doctor's ability to diagnose COVID-19 ( p < 0.001). There were significantly more Filipino respondents who were worried about their family members contracting COVID-19 ( p < 0.001). For health information, there were significantly more Filipino who were unsatisfied with the amount of health information ( p < 0.001) and spent more than three hours per day on the news related to COVID-19 ( p < 0.001). There were significantly more Chinese respondents who felt ostracized by other countries ( p < 0.001).

Linear regression analysis after adjustment of confounding factors showed that the Filipino and Chinese respondents showed different findings (see Table 4 ). Chinese respondents who reported a very low perceived likelihood of contracting COVID-19 were significantly associated with lower DASS depression scores ( p < 0.05). There were similarities between the two countries. Filipino and Chinese respondents who perceived a very high likelihood of survival were significantly associated with lower DASS-21 depression scores ( p < 0.05). Regarding the level of confidence in the doctor's ability to diagnose COVID-19, both Filipino and Chinese respondents who were very confident in their doctors were significantly associated with lower DASS-21 depression scores ( p < 0.01). Filipino and Chinese respondents who were satisfied with health information were significantly associated with lower DASS-21 anxiety and depression scores ( p < 0.01). Chinese and Filipino respondents who were worried about their family members contracting COVID-19 were associated with higher IES-R and DASS-21 subscale scores ( p < 0.05). In contrast, only Filipino respondents who spent <1 h per day monitoring COVID-19 information was significantly associated with lower IES-R and DASS-21 stress and anxiety scores ( p < 0.05). Filipino respondents who felt ostracized were associated with higher IES-R and stress scores ( p < 0.05).

Table 4 . Comparison of association of knowledge and concerns related to COVID-19 with mental health status after adjustment for age, gender, and marital status ( N = 1,710).

Health Information About COVID-19 and Its Association With Psychological Impact and Adverse Mental Health Status

Filipino (LMIC) and Chinese (UMIC) respondents held significantly different views on the information required about COVID-19. There were significantly more Chinese respondents who needed information on the symptoms related to COVID-19, prevention methods, management and treatment methods, regular information updates, more personalized information, the effectiveness of drugs and vaccines, number of infected by geographical locations, travel advice and transmission methods as compared to Filipino ( p < 0.01; See Supplementary Table 3 ). In contrast, there were significantly more Filipino respondents who needed information on other countries' strategies and responses than Chinese ( p < 0.001).

Information on management methods and transmission methods were significantly associated with higher IES-R scores in Chinese respondents ( p < 0.05; see Table 5 ). Travel advice, local transmission data, and other countries' responses were significantly associated with lower DASS-21 stress and depression scores in Chinese respondents only ( p < 0.05). There was only one significant association observed in Filipino respondents; information on transmission methods was significantly associated with lower DASS-21 depression scores ( p < 0.05).

Table 5 . Comparison of the association between information needs about COVID-19 and the psychological impact as well as adverse mental health status between Filipino (LMIC) and Chinese (UMIC) participants after adjustment for age, gender, and marital status ( N = 1,710).

To our best knowledge, this is the first study that compared the physical and mental health as well as knowledge, attitude and belief about COVID-19 between citizens from an LMIC (The Philippines) and UMIC (China). Filipino respondents reported significantly higher levels of depression, anxiety and stress than Chinese during the COVID-19, but only the mean IES-R scores of Chinese respondents were above the cut-off scores for PTSD symptoms. Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection, recent use of medical services with lower confidence, recent direct, and indirect contact with COVID, concerns about family members contracting COVID-19 and dissatisfaction with health information. In contrast, Chinese respondents requested more health information about COVID-19 and were more likely to stay at home for more than 20–24 h per day. For the Filipino, student status, low confidence in doctors, unsatisfaction of health information, long hours spent on health information, worries about family members contracting COVID-19, ostracization, unnecessary worries about COVID-19 were associated with adverse mental health.

The most important implication of the present study is to understand the challenges faced by a sample of people from an LMIC (The Philippines) compared to a sample of people from a UMIC (China) in Asia. As physical symptoms resembling COVID-19 infection (e.g., headache, myalgia, dizziness, and coryza) were associated with adverse mental health in both countries, this association could be due to lack of confidence in healthcare system and lack of testing for coronavirus. Previous research demonstrated that adverse mental health such as depression could affect the immune system and lead to physical symptoms such as malaise and other somatic symptoms ( 39 , 40 ). Based on our findings, the strategic approach to safeguard physical and mental health for middle-income countries would be cost-effective and widely available testing for people present with COVID-19 symptoms, providing a high quality of health information about COVID-19 by health authorities.

Students were afraid that confinement and learning online would hinder their progress in their studies ( 41 ). This may explain why students from the Philippines reported higher levels of IES-R and depression scores. Schools and colleges should evaluate the blended implementation of online and face-to-face learning to optimize educational outcomes when local spread is under control. As a significantly higher proportion of Filipino respondents lack confidence in their doctors, health authorities should ensure adequate training and develop hospital facilities to isolate COVID-19 cases and prevent COVID-19 spread among healthcare workers and patients ( 42 ). Besides, our study found that Filipino respondents were dissatisfied with health information. In contrast, Chinese respondents demanded more health information related to COVID-19. The difference could be due to stronger public health campaign launched by the Chinese government including national health education campaigns, a health QR (Quick Response) code system and community engagement that effectively curtailed the spread of COVID-19 ( 43 ). The high expectation for health information could be explained by high education attainment of participants as about 91.4 and 87.6% of participants from China and the Philippines have a university education.

Furthermore, the governments must employ communication experts to craft information, education, and messaging materials that are target-appropriate to each level of understanding in the community. That the Chinese Government rapidly deployed medical personnel and treated COVID-19 patients at rapidly-built hospitals ( 44 ) is in itself a confidence-building measure. Nevertheless, recent quarantine was associated with higher DASS-21 subscale scores in Chinese respondents only. It could be due to stricter control and monitoring of movements imposed by the Chinese government during the lockdown ( 45 ). Chinese respondents who stayed with more than three family members were associated with higher IES-R scores. The high IES-R scores could be due to worries of the spread of COVID-19 to family members and overcrowded home environment during the lockdown. The Philippines also converted sports arena into quarantine/isolation areas for COVID-19 patients with mild symptoms. These prompt actions helped restore public confidence in the healthcare system ( 46 ). A recent study reported that cultural factors, demand pressure for information, the ease of information dissemination via social networks, marketing incentives, and the poor legal regulation of online contents are the main reasons for misinformation dissemination during the COVID-19 pandemic ( 47 ). Bastani and Bahrami ( 47 ) recommended the engagement of health professionals and authorities on social media during the pandemic and the improvement of public health literacy to counteract misinformation.

Chinese respondents were more likely to feel ostracized and Filipino respondents associated ostracization with adverse mental health. Recently, the editor-in-chief of The Lancet , Richard Horton, expressed concern of discrimination of a country or particular ethnic group, saying that while it is important to understand the origin and inter-species transmission of the coronavirus, it was both unhelpful and unscientific to point to a country as the origin of the Covid-19 pandemic, as such accusation could be highly stigmatizing and discriminatory ( 48 ). The global co-operation involves an exchange of expertise, adopting effective prevention strategies, sharing resources, and technologies among UMIC and LIMC to form a united front on tackling the COVID-19 pandemic remains a work in progress.

Strengths and Limitations

The main strength of this study lay in the fact that we performed in-depth analysis and studied the relationship between physical and mental outcomes and other variables related to COVID-19 in the Philippines and China. However, there are several limitations to be considered when interpreting the results. Although the Philippines is a LMIC and China is a UMIC, the findings cannot be generalized to other LIMCs and UMICs. Another limitation was the potential risk of sampling bias. This bias could be due to the online administration of questionnaires, and the majority of respondents from both countries were respondents with good educational attainment and internet access. We could not reach out to potential respondents without internet access (e.g., those who stayed in the countryside or remote areas). Further, our findings may not be generalizable to other middle-income countries.

During the COVID-19 pandemic, Filipinos (LMIC) respondents reported significantly higher levels of depression, anxiety and stress than Chinese (UMIC). Filipino respondents were more likely to report physical symptoms resembling COVID-19 infection, recent use of medical services with lower confidence, recent direct and indirect contact with COVID, concerns about family members contracting COVID-19 and dissatisfaction with health information than Chinese. For the current COVID-19 and future pandemic, Middle income countries need to adopt the strategic approach to safeguard physical and mental health by establishing cost-effective and widely available testing for people who present with COVID-19 symptoms; provision of high quality and accurate health information about COVID-19 by health authorities. Our findings urge middle income countries to prevent ostracization of a particular ethnic group, learn from each other, and unite to address the challenge of the COVID-19 pandemic and safeguard physical and mental health.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. The Institutional Review Board of the University of Philippines Manila Research Ethics Board (UPMREB 2020- 198-01) and Huaibei Normal University (China) approved the research proposal (HBU-IRB-2020-002).

Author Contributions

Concept and design: CW, MT, CT, RP, VK, and RH. Acquisition, analysis, and interpretation of data: CW, MT, CT, RP, LX, CHa, XW, YT, and VK. Drafting of the manuscript: CW, MT, CT, RH, and JA. Critical revision of the manuscript: MT, CT, CHo, and JA. Statistical analysis: CW, PR, RP, LX, XW, and YT. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2020.568929/full#supplementary-material

1. Sohrabi C, Alsafi Z, O'Neill N, Khan M, Kerwan A, Al-Jabir A, et al. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. (2020) 76:71–6. doi: 10.1016/j.ijsu.2020.02.034

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Mahase E. Covid-19: WHO declares pandemic because of “alarming levels” of spread, severity, and inaction. BMJ. (2020) 368:m1036. doi: 10.1136/bmj.m1036

3. Singhal T. A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatr. (2020) 87:281–6. doi: 10.1007/s12098-020-03263-6

CrossRef Full Text | Google Scholar

4. Oxford. Oxford U of. Coronavirus Disease (COVID-19) - Statistics and Research . (2020). Available online at: https://ourworldindata.org/coronavirus%0D (accessed May 17, 2020).

Google Scholar

5. Edrada EM, Lopez EB, Villarama JB, Salva Villarama EP, Dagoc BF, Smith C, et al. First COVID-19 infections in the Philippines: a case report. Trop Med Health. (2020) 48:21. doi: 10.1186/s41182-020-00218-7

6. CNN. TIMELINE: How the Philippines is Handling COVID-19. (2020). Available online at: https://cnnphilippines.com/news/2020/4/21/interactive-timeline-PH-handling-COVID-19.html (accessed May 17, 2020).

7. Worldmeters. COVID-19 Coronavirus Pandemic. (2020). Available online at: https://www.worldometers.info/coronavirus/ (accessed December 4 2020).

8. WorldBank. World Bank National Accounts Data, and OECD National Accounts Data Files . (2020). Available online at: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD (accessed April 16 2020).

9. Schellekens P, Sourrouille D. The Unreal Dichotomy in COVID-19 Mortality between High-Income and Developing Countries. (2020). Available online at: https://www.brookings.edu/blog/future-development/2020/05/05/the-unreal-dichotomy-in-covid-19-mortality-between-high-income-and-developing-countries/ (accessed May 10 2020).

10. Medicalexpress. ‘Slow Response’: How Britain Became Worst-Hit in Europe by Virus. (2020). Available online at: https://medicalxpress.com/news/2020-05-response-britain-worst-hit-europe-virus.html (accessed May 8 2020).

11. Mamun MA, Ullah I. COVID-19 suicides in Pakistan, dying off not COVID-19 fear but poverty? - The forthcoming economic challenges for a developing country. Brain Behav Immun. (2020) 87:163–6. doi: 10.1016/j.bbi.2020.05.028

12. Kar SK, Oyetunji TP, Prakash AJ, Ogunmola OA, Tripathy S, Lawal MM, et al. Mental health research in the lower-middle-income countries of Africa and Asia during the COVID-19 pandemic: a scoping review. Neurol Psychiatry Brain Res. (2020) 38:54–64. doi: 10.1016/j.npbr.2020.10.003

13. Leung GM, Lam TH, Ho LM, Ho SY, Chan BH, Wong IO, et al. The impact of community psychological responses on outbreak control for severe acute respiratory syndrome in Hong Kong. J Epidemiol Community Health. (2003) 57:857–63. doi: 10.1136/jech.57.11.857

14. Cristóbal-Narváez P, Haro JM, Koyanagi A. Perceived stress and depression in 45 low- and middle-income countries. J Affect Disord. (2020) 274:799–805. doi: 10.1016/j.jad.2020.04.020

15. Vallejo MA, Vallejo-Slocker L, Fernández-Abascal EG, Mañanes G. Determining factors for stress perception assessed with the perceived stress scale (PSS-4) in Spanish and other European samples. Front Psychol. (2018) 9:37. doi: 10.3389/fpsyg.2018.00037

16. Shin C, Kim Y, Park S, Yoon S, Ko YH, Kim YK, et al. Prevalence and associated factors of depression in general population of Korea: results from the Korea National Health and Nutrition Examination Survey, 2014. J Korean Med Sci. (2017) 32:1861–9. doi: 10.3346/jkms.2017.32.11.1861

17. Olsen LR, Mortensen EL, Bech P. Prevalence of major depression and stress indicators in the Danish general population. Acta Psychiatr Scand. (2004) 109:96–103. doi: 10.1046/j.0001-690X.2003.00231.x

18. Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of depression in the community from 30 countries between 1994 and (2014). Sci Rep. (2018) 8:2861. doi: 10.1038/s41598-018-21243-x

19. Sang T, Zhou H, Li M, Li W, Shi H, Chen H, et al. Investigation of the differences between the medical personnel's and general population's view on the doctor-patient relationship in China by a cross-sectional survey. Global Health. (2020) 16:99. doi: 10.21203/rs.3.rs-41725/v2

20. Ferrer RR, Ramirez M, Beckman LJ, Danao LL, Ashing-Giwa KT. The impact of cultural characteristics on colorectal cancer screening adherence among Filipinos in the United States: a pilot study. Psychooncology. (2011) 20:862–70. doi: 10.1002/pon.1793

21. Lei P, Jolibert A. A three-model comparison of the relationship between quality, satisfaction and loyalty: an empirical study of the Chinese healthcare system. BMC Health Serv Res. (2012) 12:436. doi: 10.1186/1472-6963-12-436

22. Peabody JW, Florentino J, Shimkhada R, Solon O, Quimbo S. Quality variation and its impact on costs and satisfaction: evidence from the QIDS study. Med Care. (2010) 48:25–30. doi: 10.1097/MLR.0b013e3181bd47b2

23. Xiong J, Lipsitz O, Nasri F, Lui LMW, Gill H, Phan L, et al. Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J Affect Disord. (2020) 277:55–64. doi: 10.1016/j.jad.2020.08.001

24. Wang C, Pan R, Wan X, Tan Y, Xu L, McIntyre RS, et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav Immun. (2020) 87:40–8. doi: 10.1016/j.bbi.2020.04.028

25. Zhang MW, Ho CS, Fang P, Lu Y, Ho RC. Usage of social media and smartphone application in assessment of physical and psychological well-being of individuals in times of a major air pollution crisis. JMIR Mhealth Uhealth. (2014) 2:e16. doi: 10.2196/mhealth.2827

26. Chew NWS, Lee GKH, Tan BYQ, Jing M, Goh Y, Ngiam NJH, et al. A multinational, multicentre study on the psychological outcomes and associated physical symptoms amongst healthcare workers during COVID-19 outbreak. Brain Behav Immun. (2020) 88:559–65. doi: 10.1016/j.bbi.2020.04.049

27. Zhang MWB, Ho CSH, Fang P, Lu Y, Ho RCM. Methodology of developing a smartphone application for crisis research and its clinical application. Technol Health Care. (2014) 22:547–59. doi: 10.3233/THC-140819

28. Creamer M, Bell R, Failla S. Psychometric properties of the Impact of Event Scale - Revised. Behav Res Ther. (2003) 41:1489–96. doi: 10.1016/j.brat.2003.07.010

29. Lee SM, Kang WS, Cho A-R, Kim T, Park JK. Psychological impact of the 2015 MERS outbreak on hospital workers and quarantined hemodialysis patients. Compr Psychiatry. (2018) 87:123–7. doi: 10.1016/j.comppsych.2018.10.003

30. Le TA, Le MQT, Dang AD, Dang AK, Nguyen CT, Pham HQ, et al. Multi-level predictors of psychological problems among methadone maintenance treatment patients in difference types of settings in Vietnam. Subst Abuse Treat Prev Policy. (2019) 14:39. doi: 10.1186/s13011-019-0223-4

31. Cheung JTK, Tsoi VWY, Wong KHK, Chung RY. Abuse and depression among Filipino foreign domestic helpers. A cross-sectional survey in Hong Kong. Public Health. (2019) 166:121–7. doi: 10.1016/j.puhe.2018.09.020

32. Tee ML, Tee CA, Anlacan JP, Aligam KJG, Reyes PWC, Kuruchittham V, et al. Psychological impact of COVID-19 pandemic in the Philippines. J Affect Disord. (2020) 277:379–91. doi: 10.1016/j.jad.2020.08.043

33. Tee CA, Salido EO, Reyes PWC, Ho RC, Tee ML. Psychological State and Associated Factors During the 2019 Coronavirus Disease (COVID-19) pandemic among filipinos with rheumatoid arthritis or systemic lupus erythematosus. Open Access Rheumatol. (2020) 12:215–22. doi: 10.2147/OARRR.S269889

34. Ho CSH, Tan ELY, Ho RCM, Chiu MYL. Relationship of anxiety and depression with respiratory symptoms: comparison between depressed and non-depressed smokers in Singapore. Int J Environ Res Public Health. (2019) 16:163. doi: 10.3390/ijerph16010163

35. Quek TC, Ho CS, Choo CC, Nguyen LH, Tran BX, Ho RC. Misophonia in Singaporean psychiatric patients: a cross-sectional study. Int J Environ Res Public Health. (2018) 15:1410. doi: 10.3390/ijerph15071410

36. Hao F, Tan W, Jiang L, Zhang L, Zhao X, Zou Y, et al. Do psychiatric patients experience more psychiatric symptoms during COVID-19 pandemic and lockdown? A case-control study with service and research implications for immunopsychiatry. Brain Behav Immun. (2020) 87:100–6. doi: 10.1016/j.bbi.2020.04.069

37. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 Coronavirus Disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health. (2020) 17:E1729. doi: 10.3390/ijerph17051729

38. Tan BYQ, Chew NWS, Lee GKH, Jing M, Goh Y, Yeo LLL, et al. Psychological impact of the COVID-19 pandemic on health care workers in Singapore. Ann Intern Med . (2020) 173:317–20. doi: 10.7326/M20-1083

39. Liu Y, Ho RC, Mak A. Interleukin (IL)-6, tumour necrosis factor alpha (TNF-α) and soluble interleukin-2 receptors (sIL-2R) are elevated in patients with major depressive disorder: a meta-analysis and meta-regression. J Affect Disord. (2012) 139:230–9. doi: 10.1016/j.jad.2011.08.003

40. Viljoen M, Panzer A. Proinflammatory cytokines: a common denominator in depression and somatic symptoms? Can J Psychiatry. (2005) 50:128. doi: 10.1177/070674370505000216

41. Li HY, Cao H, Leung DYP, Mak YW. The psychological impacts of a COVID-19 outbreak on college students in China: a longitudinal study. Int J Environ Res Public Health. (2020) 17:3933. doi: 10.3390/ijerph17113933

42. Krishnakumar B, Rana S. COVID 19 in INDIA: strategies to combat from combination threat of life and livelihood. J Microbiol Immunol Infect. (2020) 53:389–91. doi: 10.1016/j.jmii.2020.03.024

43. Huang Y, Wu Q, Wang P, Xu Y, Wang L, Zhao Y, et al. Measures undertaken in China to avoid COVID-19 infection: internet-based, cross-sectional survey study. J Med Internet Res. (2020) 22:e18718. doi: 10.2196/18718

44. Salo J. China Orders 1,400 Military Doctors, Nurses to Treat Coronavirus. (2020). Available online at: https://nypost.com/2020/02/02/china-orders-14000-military-doctors-nurses-to-treat-coronavirus/ (accessed March 22, 2020).

45. Burki T. China's successful control of COVID-19. Lancet Infect Dis. (2020) 20:1240–1. doi: 10.1016/S1473-3099(20)30800-8

46. Esguerra DJ. Philippine Arena to Start Accepting COVID-19 Patients Next Week . (2020). Available online at: https://newsinfo.inquirer.net/1255623/philippine-arena-to-start-accepting-covid-19-patients-next-week (accessed November 18, 2020).

47. Bastani P, Bahrami MA. COVID-19 related misinformation on social media: a qualitative study from Iran. J Med Internet Res. (2020). doi: 10.2196/preprints.18932. [Epub ahead of print].

48. Catherine W. It's Unfair to Blame China for Coronavirus Pandemic, Lancet Editor Tells State Media. (2020). Available online at: https://www.scmp.com/news/china/science/article/3082606/its-unfair-blame-china-coronavirus-pandemic-lancet-editor-tells (accessed May 8, 2020).

Keywords: anxiety, China, COVID-19, depression, middle-income, knowledge, precaution, Philippines

Citation: Tee M, Wang C, Tee C, Pan R, Reyes PW, Wan X, Anlacan J, Tan Y, Xu L, Harijanto C, Kuruchittham V, Ho C and Ho R (2021) Impact of the COVID-19 Pandemic on Physical and Mental Health in Lower and Upper Middle-Income Asian Countries: A Comparison Between the Philippines and China. Front. Psychiatry 11:568929. doi: 10.3389/fpsyt.2020.568929

Received: 02 June 2020; Accepted: 22 December 2020; Published: 09 February 2021.

Reviewed by:

Copyright © 2021 Tee, Wang, Tee, Pan, Reyes, Wan, Anlacan, Tan, Xu, Harijanto, Kuruchittham, Ho and Ho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Cuiyan Wang, wcy@chnu.edu.cn

† These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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

Sarbaz h.a. khoshnaw.

a Department of Mathematics, University of Raparin, Ranya, Sulaimani, Iraq

Muhammad Shahzad

b Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan

Mehboob Ali

Faisal sultan.

Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates.

This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations.

Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

1. Introduction

The history of the coronaviruses started from the 1930s when the infectious bronchitis virus (IBV) infection was caused by the domesticated chickens. In humans, it was firstly reported in the 1960s [1] . To avoid stigmatizing the virus's origins in terms of populations, geography, or animal associations the WHO termed as COVID-19 in December 2019. This virus involves serious respiratory tract infections [ 2 , 3 ]. Detected in Wuhan City, Hubei Province of China, this virus might be transferred to the seafood market [ 4 , 5 ] and then internationally via domestic/commercial air traveler carriers [6] . After that, an emergency has been declared in infected areas of the world and a serious public health concern has been paid at a global level. While to stop the impact of this infection and to avoid further spreading some mathematical estimations are also being performed at each level [7] .

Spreading the novel coronavirus (COVID-19) has become a very difficult global issue. For May 15th, 2020, there were 4609,787 confirmed cases, 307, 528 deaths, and 1742,813 recovered cases around the world [8] . This virus has been spread more quickly around the world, it is very important to know its dynamics and predications. Although there were several proposed studies based on mathematical tools, computational simulations, clinical, and examination tests for controlling the disease, there are still different dimensions about this issue that can be further improved.

Recently, mathematical modeling of the COVID-19 disease has been investigated by several mathematical and computational researchers for the prediction of the disease dynamics. One of the well-known concepts in epidemiology is the basic reproduction number (or it sometimes is called the basic reproductive ratio). This ratio is a quantity and it is denoted by R 0 . This is the average number of secondary infections produced by a single primary infection in a population where everyone is susceptible. Based on the developed approaches of mathematical epidemiology models, this quantity becomes an essential tool to understand epidemiological concepts and identify key critical parameters for such models. The value of this quantity has been changed for the confirmed cases of Coronavirus. According to a study published recently, the median value of R 0 was about 2.28 during the early stage experienced [9] . Based on another study, this quantity is about 2.489. They suggested a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease [10] . There is also a developed compartmental model based on the clinical progression of the disease, the epidemiological status of the individuals, and intervention measures [11] . According to this study, the estimation of the basic reproduction number is very high and it was about 6.47. The idea of the next-generation matrix approach was used to calculate the basic reproduction number for the COVID-19 models, this is presented in [12] . According to their study, this ratio is bout 3.58. Furthermore, an updated model was proposed to estimate the transmission risk of the novel coronavirus, their calculations relate to a time-dependent dynamic model of contact and diagnosis rate. This helps us to re-estimated the daily reproduction number [13] .

One of the key parameters that can be accumulated from the confirmed cases is the serial interval. The serial interval is a time interval between primary patients (infecters) and the second patient (infectees). This parameter helps investigators to find epidemic transmission links between confirmed cases and uses for design healthcare program strategies [14] . The serial interval is widely used for describing models dynamics of infectious disease. This may help us to understand the effect of contact among individuals and how transmits the virus more quickly. Recently, the serial interval has been calculated for the COVID-19, this estimates by 4.6 days [15] . A reported study mentioned that the mean serial interval for COVID-19 was calculated as 3.96 days. This considerably shorter than the mean serial interval calculated for SARS 8.4 days and MERS 14.6 days [16] .

Although, there is a variety of modeling approaches that have been proposed so far for prediction and evaluation of the COVID-19 disease, there are more questions that can still be answered. There are some related works that sensitivity analysis used in their studies [ 13 , 17 ]. They used this technique only for special variables and parameters. More recently, mathematical modeling has been used to describe this disease in different views. Transmission and mitigation strategies for COVID-19 presented in [18] , response strategies for COVID-19 epidemics in African settings shown in [19] , the effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic explained in [20] , mathematical recommendations to fight against COVID-19 investigated in [21] , the feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts presented in [22] , a mathematical modeling approach in the Spread of the COVID-19 described in [23] , the reproductive number of COVID-19 is higher compared to SARS coronavirus presented in [24] .

Defining such proposed models based on the mass action law with transmission parameters and identify some critical model elements could improve the outcomes further. An issue that has not been explained very well for this new virus is identifying the local sensitivities for non-normalizations, half normalizations, and full normalization techniques in commotional simulations. Here, we are proposing a mathematical model to estimate the transmissibility with the dynamical view of the virus transmission. For the complex model of COVID-19 given in this paper, it is vital to pay more attention to identify critical model elements based on sensitivity analysis and computational results more widely and accurately. Another contribution of this paper is analyzing the suggested model based on different values of the model parameters in commotional simulations. This is also an important step forward to understand how changing the model dynamics and predicting the spread of this virus in the future. The main contribution here is to understand the impact of each model parameter on the model states, this is given in our computational simulations and calculating the local sensitivities. This will help to quantify possible disease control strategies with further improvements and recommendations. Results in this study provide effective roles in further suggestions and preventions theoretically and practically. On possible theoretical suggestion is that these results could help biologists to understand the model dynamics and parameter transmissions. This may give further improvements and analyze the complex models in computational simulations. While the results here could also give practical suggestions. One practical improvement for controlling this disease could be reducing the contact between asymptomatic infected, reported, and unreported infected classes because their transmission parameters are very sensitive based on our computational simulations.

The idea of chemical kinetic theory is an important approach for understanding and representing the biological process in terms of model equations. The important assumptions to build such models are model states, parameters, and equations. This is because it helps the investigation of mathematical modeling effectively and easily [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] . Therefore, we present here the basics of our calculations. We consider n irreversible chemical reactions and m  chemical components with a stoichiometric equation given below

where x j for j = 1 , 2 , … , m are chemical species, a ij  and  b ij are stoichiometric coefficients (non-negative integers), α i > 0 , i = 1 , 2 , … , n are chemical interaction parameters. The model reaction rates are simply defined based on the classical theory of mass-action law

The chemical reactions can be expressed as a system of differential equations with initial components

where π i = b i j − a i j , for i = 1 , 2 , … , n and j = 1 , 2 , … , m . Therefore, Eq. (3) takes the form

where x ∈ R m and α ∈ R n . The functions h j , j = 1 , 2 , … , m are often non-linear therefore model differential equations may not solve analytically. The more details and descriptions about chemical reactions and their differential equations with some applications in system biology can be found in [ 25 , 36 , 37 ]. An important technique to analyze system 4 is the idea of sensitivity analysis. According to this approach, the sensitivity of each variable concerning parameters can be calculated. The main equation of sensitivity is given below

The first order derivatives given in Eq. (5) represent the time-dependent sensitivities of all variables { x j , j = 1 , 2 , … , m } concerning each parameter value { α p , p = 1 , 2 , … , n }

Furthermore, the differential equations can be solved for sensitivity coefficients as below

Using the chain rule of differentiation, Eq. (6) can be further driven and the sensitivity equations take the Jacobian matrix as follows

where the matrices S , H α p and J are defined by

For more details and applications of sensitivity analysis in the field systems biology, the reader can see in [ 25 , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] ]. The local sensitivity values are given in Eq. (7) can be computed using SimBiology Toolbox in MATLAB with three different techniques: non-normalizations, half normalizations, and full normalizations. Accordingly, in a complicated modeling case like new coronavirus dynamics, it is necessary to pay attention to sensitivity analysis more accurately and widely. This provides one to identify the key critical model parameters and to improve model dynamics.

3. An updated model for coronavirus disease

The classical models of epidemic diseases normally include components (individuals) and interaction changes among components in the population. In other words, for an epidemic network model, nodes are individuals, and edges are contacted among individuals. This representation helps us to understand infectious disease models in terms of a graphical network. There are some recently proposed models for presenting the dynamical equations of COVID-19 [26] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] . The model initial populations and interaction parameters are mainly obtained for the confirmed cases in China. The main idea of the models is based on clinical progression, epidemiological individuals, and intervention measures. In this paper, we develop the previous model presented in [55] . We then extend the suggested model structure by including the recovered individuals (R), the unreported symptomatic death rate ( α 1 U ) and reported symptomatic death rate ( α 2 W ). The model network diagram and the interaction individual components demonstrated in Fig. 1 . The COVID-19 model digraph can be expressed as a graph G = ( N , L ) , where G is the model graph, N = ( S , I , U , W , R ) is the set of nodes (states), L = { v 1 , v 2 , v 3 , v 4 , v 5 , v 6 , v 7 } is the set of links (reactions).

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

Thus, let us consider the human groups separated into 5 different groups.

• 1 Susceptible group (S). This group presents a healthy individual.
• 2 Asymptomatic group (I). This group presents an infected individual in the early stage of infection. They do not show any symptoms, but capable to infect through droplets or direct contact with the susceptible individual.
• 3 Symptomatic unreported group (U). This group presents an individual who gets infected, had symptoms of COVID-19 but did not detect by the government as a COVID-19 suspect.
• 4 Symptomatic reported group (W). This group presents an individual who gets infected, shows symptoms of COVID-19, and detected by the government, either from a rapid test or from voluntary action to report to the hospital. We assume that all individuals in this class will get a specific treatment and supervision, whether it's through monitored isolation or treatment in the hospital.
• 5 Recovered group (R). This group present individual who get recovered from COVID-19, and had a temporal immunity.

The transmission diagram which illustrates the interaction between each group described in Fig. 1 . The developed model here has six parameters that can be analyzed in computational simulations. They are defined as interactions rate constants between individual groups. The contacts between different states (individuals) are as follows. Susceptible people ( S ) move into the infected people ( I ) when they are asymptomatic infected at the transition rate β S ( I + U ) . After that, the asymptomatic infected individuals ( I ) either move into the reported symptomatic infected individuals ( W ) with the transition rate γI or turn into the unreported symptomatic infected individuals ( U ) with transition rate δI . Both groups W and U move into the recovered individuals ( R ) with the transition rates ηW and ηU , respectively. In addition to that some unreported and reported infected individuals may die with transition rates α 1 U and α 2 W , respectively.

The model reactions with their rats are shown below:

where v 1 = β S ( I + U ) , v 2 = γ I , v 3 = δ I , v 4 = η W , v 5 = η U , v 6 = α 1 U ,

Furthermore, the model's constant parameters and initial states with their definitions are described in Table 1 . Using Eqs. (2) – (4) , the model dynamics are described by the following system of non-linear ordinary differential equations

The model reaction constants (parameters) and initial individual populations for COVID–19 epidemic outbreak with their biological definitions, all data are confirmed cases in China presented in [55] .

The model initial populations are expressed in the following equation

6. Computational results

Analyzing epidemic disease models with a high number of elements sometimes cannot be well understood only by biological tools. This is why mathematical models are used, such models help and provide a good environment to analyze infectious diseases more widely and easily. Theoretical studies may help biologists to predict future model dynamics and identify critical model parameters. In the case of COVID-19, the suggested mathematical models are effective tools that give estimations and suggestions about controlling the virus and further preventions more effectively.

The values of parameters and initial populations in this study are obtained from the WHO situation report (the National Health Commission of the Republic of China) presented in [55] . We calculate the numerical approximate solutions of the model equations (9) for different parameters and initial populations using System Biology Toolbox (SBedit) for MATLAB; see Fig. 2 , Fig. 3 , Fig. 4 , Fig. 5 , Fig. 6 . Accordingly, there are different model dynamics for initial population model states and estimated parameters. Computational numerical simulations are calculated in two-dimensional planes for model parameters and initial populations. Results in this study provide a good step forward in predicting the model dynamics in the future for development programs, interventions, and health care strategies.

Computational simulations for the model states given in system (9) of the COVID–19 using MATLAB; there are model dynamics of (a) susceptible individuals, (b) asymptomatic infected individuals, (c) unreported symptomatic infected individuals, (d) reported symptomatic infected individuals, (e) recovered individuals.

Computational simulations for the model states given in system (9) of the COVID–19 using MATLAB; there is the relationship between the asymptomatic infected people and (a) susceptible individuals, (b) unreported symptomatic infected individuals, (c) reported symptomatic infected individuals, (d) recovered individuals.

The effect of transition rate δ on (a) asymptomatic infected individuals, (b) unreported symptomatic infected individuals, (c) reported symptomatic infected individuals, in computational simulations using MATLAB parameters used δ = 0.0285 , 0.15 , 0.6 .

The effect of transition rate γ on (a) asymptomatic infected people, (b) unreported symptomatic infected people, (c) reported symptomatic infected people, in computational simulations using MATLAB parameters used γ  = 0.1142, 0.26, 0.32.

The effect of parameter η on (a) unreported symptomatic infected people, (b) reported symptomatic infected people, (c) recovered people, in computational simulations using MATLAB parameters used η  = 0.1428, 0.05, 0.02.

Fig. 2 shows the model dynamics of susceptible, asymptomatic infected, reported symptomatic, and unreported symptomatic individuals. The number of susceptible populations decreases gradually and becomes stable after 60 days while the dynamics of recovered people increase and get flat again after 60 days. Interestingly, the number of infected individuals in both asymptomatic and symptomatic groups is dramatically changed between 40 days to 80 days. Furthermore, Fig. 3 explains the relationship between infected people with the other groups in the COVID-19. It can be seen that there are the same dynamical relations for reported and unreported symptomatic states whereas there are slightly different model dynamics for susceptible and recovered groups.

Fig. 4 shows that the impact of transition rate δ  on asymptomatic infected reported symptomatic infected and unreported symptomatic infected individuals. The effect of this parameter can easily occur on the variables  I, U , and  W . For example, if the value of δ is increased from 0.0285 to 0.6 then the number of unreported symptomatic infected people is dramatically increased, see Fig. 4 (b). On the other hand, there will be more asymptomatic infected and reported symptomatic infected cases when the values of transition rate δ  become smaller and smaller, see Figs. 4 (a) and 4(c).

Fig. 5 shows that the impact of transition rate γ on asymptomatic infected people reported symptomatic infected people and unreported symptomatic infected people. The effect of this parameter can easily occur on the variables I ,  U , and  W . It can be seen that the model dynamics for such states become more flat when the value of γ is increased. This is an important key element for controlling this disease.

Fig. 6 shows that the impact of parameter η (average time that infected people have symptoms) on the dynamics of unreported symptomatic, reported symptomatic, and recovered individuals. The number of both unreported and reported infected people are significantly increased when the value of η becomes smaller, see Figs. 6 (a) and 6(b). Conversely, the dynamics of recovered people dramatically grow and become stable very quickly when the value of η gets bigger and bigger, see Fig. 6 (c)

Another important step of parameter analysis is using the idea of local sensitivity analysis presented in Eq. (7) . We use SimBiology Toolbox for MATLAB to calculate the local sensitivity of each model state concerning model parameters for the model equations (9) . We compute the model sensitivities using three different techniques: non-normalizations, half normalizations, and full normalizations; see Figs. 7 . Interestingly, results provide us further understanding of the model and give one to identify the key critical model parameters. For example, it generally seems that the communication rate between asymptomatic infected and unreported symptomatic infected, transition rate between asymptomatic infected and reported symptomatic infected, the average time that infected people have symptoms are very sensitive parameters concerning model variables in spreading this disease among people. On the other hand, the other parameters are less sensitive to the model dynamics, they are unreported symptomatic death rate, reported symptomatic death rate and transition rate between susceptible and asymptomatic infected people. As a result, identifying critical model parameters in this study based on computational simulations is an effective way to further study the model practically and theoretically and give some suggestions for future improvements of the novel coronavirus vaccination programs, interventions, and controlling the spread of disease.

The sensitivity of each model state concerning model parameters in computational simulations for the coronavirus disease (COVID–19); (a) non-normalization sensitivity, (b) half normalization sensitivity, (c) full normalization sensitivity.

Accordingly, some findings and computational results are given based on the suggested approaches for the updated model of the COVID-19. We can mainly summarize them as the points given below:

• 1 The model dynamics of susceptible, asymptomatic infected, both reported and unreported symptomatic individuals, recovered individuals are computed using System Biology Toolbox (SBedit) in MATLAB. It can be seen that the relationship between infected people with the other groups in the COVID-19 model. There are the same dynamical relations for reported and unreported symptomatic states whereas there are slightly different model dynamics for susceptible and recovered groups.
• 2 The impact of transition rate δ  on asymptomatic infected reported symptomatic infected and unreported symptomatic infected individuals is investigated. This parameter has an effective role in the dynamics of I, U , and  W .
• 3 The transition rate γ has also affected asymptomatic infected people, reported symptomatic infected people, and unreported symptomatic infected people. It can be seen that the model dynamics for such states become more flat when the value of γ is increased. This is an important key element for controlling this disease.
• 4 The number of both unreported and reported infected people are significantly increased when the value of η becomes smaller whereas the dynamics of recovered people dramatically grow and become stable very quickly when the value of η becomes large.
• 5 The local sensitivity of each model state concerning the model parameters computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Interestingly, results provide us further understanding and identifying the key critical model parameters. It can be concluded that the transmission parameters between asymptomatic infected and both reported and unreported symptomatic infected, the average time that infected people have symptoms are very sensitive parameters concerning model variables in computational simulations.

7. Discussions

Several healthcare strategies and preventions have been discussed and suggested as global efforts to reduce the impact of the new COVID-19 around the world. Although, this virus still spreads quickly and millions of people have been infected so far, but all scientific attempts give a great step forward to control this disease and minimizing the impact of such threats on the community. Currently, there are a variety of studies both theoretically and practically discussed and investigated in this area. One of the effective tools that could be used to describe such diseases is mathematical modeling. Mathematical modeling with computational simulations provides estimations and predictions about the dynamics of model states and parameters. However, having many suggested models in different countries makes the parameter estimations, predictions, and understandings of the disease remain unclear.

An issue that has not been well discussed and explained for this new virus is identifying the model critical elements. In this study, we computed the model dynamics for the different model parameter values. This provides us how the number of each model class changes concerning to the model parameters. Based on the results obtained here, we can simply understand the model critical parameters. For instance, the transmission parameter between asymptomatic class and the unreported symptomatic class becomes a key critical element and it has a great role in speeding this disease. However, the key critical model parameters may slightly change if we use different data reported for another country. Therefore, it can be argued that computational results and simulations based on the suggested models may give good approximations and predictions.

Another issue that can be discussed for the COVID-19 is the identification of the model sensitive parameters. This has also not well explained recently. In this work, we have used the idea of local sensitivities for non-normalizations, half normalizations, and full normalizations techniques in commotional simulations. For the complex model of COVID-19 given in this paper, it is vital to pay more attention to identify critical model elements based on sensitivity analysis and computational results more widely and accurately. Results in this paper become an important step forward to understand the model dynamics more widely. This provides to identify critical model parameters and how each model individual can be affected by the other model individuals. For example, results here show that almost all model states are sensitive to the critical model parameters {  γ ,  δ ,  η } . On the other hand, such critical model parameters can be changed to the other model parameters when we use another set of initial states and parameters. Interestingly, the suggested approach here can be further developed and applied to a wide range of complex models of the COVID-19. It can work more effectively for identifying the model key elements. This provides us the suggested approaches here work very well for the COVID-19 models compared to the recently suggested techniques.

8. Conclusions

Global efforts and preventions of the speeding COVID-19 cannot be well understood only by biological and healthcare tools. Some mathematical models have been investigated for further improvements and give estimations for such model parameters. An issue that can be further studied is identifying key critical model parameters. This helps international efforts to pay more attention to the sensitive elements. The suggested coronavirus disease (COVID–19) model here is a complicated one, and it requires some mathematical tools to have improvements in interventions and healthcare programs.

The used model in this paper has further improved based on the computational results using MATLAB for different initial populations and parameters. Some main results can help in understanding the suggested model more widely and effectively. By using computational simulation, we identify some key critical parameters that have a great role in spreading this virus among the model classes. One of the identified key parameters is the transmission rate between asymptomatic infected and reported symptomatic individuals. This is an important finding in the understanding of the COVID-19 and how this virus spreads more quickly. Some other critical model parameters have investigated in this paper. For example, the transmission parameter between asymptomatic infected and unreported symptomatic individuals has a great impact on the dynamics of the model states. Besides these findings provide additional information about estimations and predictions for the number of infected individuals. Accordingly, our results in identifying key parameters are broadly consistent with clinical and biological findings.

Remaining issues are subject to sensitivity analysis. This is also an important issue that can be further studied. We have applied the idea of local sensitivity to calculate the sensitivity of each model state concerning model parameters for the updated model of the COVID-19. Three different techniques are investigated which are non-normalizations, half normalizations, and full normalizations. These provide us an important step forward to identify critical model elements. By using local sensitivity approaches we concluded that almost all model states are sensitive to the critical model parameters {  γ ,   δ ,   η } . This becomes a great step forward and helps international attempts regarding the COVID-19 pandemic outbreak. This may help to reduce the number of infected individuals from the disease and to prevent the coronavirus more widely in the community. It can be concluded that the identified factors can be controlled to reduce the number of infected individuals. Overall, our results demonstrate a strong effect of the key critical parameters on the spreading COVID-19.

Therefore, based on the effect of each involved parameters over the model states, more suggestions and interventions can be proposed for controlling the COVID-19 disease. That will be useful for any interventions and vaccination programs. Accordingly, the healthcare communities should pay more attention to the quarantine places for controlling this disease more effectively. It can be strongly suggested that anyone in the quarantine places should be separated from the others and should use only their separate equipment, bedroom, and toilet to prevent the transmission of the virus through the touching of shared surfaces. Another suggestion is that reducing the contact between asymptomatic-symptomatic groups and susceptible groups, this is effectively minimizing the number of infected people. It seems necessary to plan a certain strategy to put the asymptomatic infected individuals on quarantine places sooner rather than later. Future research on identifying key critical elements might extend the explanations of the new COVID-19 more widely. It will be important that future research investigates more suggested transmissions between the model groups. For example, the model will further improve by adding two transmission paths, one of them is between unreported symptomatic infected and reported symptomatic infected, the other one is between asymptomatic infected and recovered individuals.

Declaration of Competing Interest

The authors declare that there are no competing interests.