Epidemiological Characteristics of COVID-19; a Systemic Review and Meta-Analysis
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Abstract
Background
Our understanding of the corona virus disease 2019 (COVID-19) continues to evolve. However, there are many unknowns about its epidemiology.
Purpose
To synthesize the number of deaths from confirmed COVID-19 cases, incubation period, as well as time from onset of COVID-19 symptoms to first medical visit, ICU admission, recovery and death of COVID-19.
Data Sources
MEDLINE, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 without language restrictions as well as bibliographies of relevant articles.
Study Selection
Quantitative studies that recruited people living with or died due to COVID-19.
Data Extraction
Two independent reviewers extracted the data. Conflicts were resolved through discussion with a senior author.
Data Synthesis
Out of 1675 non-duplicate studies identified, 57 were included. Pooled mean incubation period was 5.84 (99% CI: 4.83, 6.85) days. Pooled mean number of days from the onset of COVID-19 symptoms to first clinical visit was 4.82 (95% CI: 3.48, 6.15), ICU admission was 10.48 (95% CI: 9.80, 11.16), recovery was 17.76 (95% CI: 12.64, 22.87), and until death was 15.93 (95% CI: 13.07, 18.79). Pooled probability of COVID-19-related death was 0.02 (95% CI: 0.02, 0.03).
Limitations
Studies are observational and findings are mainly based on studies that recruited patient from clinics and hospitals and so may be biased toward more severe cases.
Conclusion
We found that the incubation period and lag between the onset of symptoms and diagnosis of COVID-19 is longer than other respiratory viral infections including MERS and SARS; however, the current policy of 14 days of mandatory quarantine for everyone might be too conservative. Longer quarantine periods might be more justified for extreme cases.
Funding
None.
Protocol registration
Open Science Framework: https://osf.io/a3k94/
Article activity feed
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SciScore for 10.1101/2020.04.01.20050138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Databases and Search Strategy: Following the Systematic Reviews and Meta-Analyses (PRISMA) checklist (16) and the Peer Review of Electronic Search Strategies (PRESS) guideline (17), we searched PubMed, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 for studies that measured and reported several characteristics of COVID-19 (e.g., incubation period, hospitalization, death) PubMedsuggested: (PubMed, RRID:SCR_004846)Embasesuggested: (EMBASE, RRID:SCR_001…SciScore for 10.1101/2020.04.01.20050138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Databases and Search Strategy: Following the Systematic Reviews and Meta-Analyses (PRISMA) checklist (16) and the Peer Review of Electronic Search Strategies (PRESS) guideline (17), we searched PubMed, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 for studies that measured and reported several characteristics of COVID-19 (e.g., incubation period, hospitalization, death) PubMedsuggested: (PubMed, RRID:SCR_004846)Embasesuggested: (EMBASE, RRID:SCR_001650)Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although this estimate is comparable with previous studies (36, 37), it is important to recognize the limitations of calculating mortality rates of COVID-19 while the epidemic is still growing. As most cases of CVOID-19 remain asymptomatic and may recover without seeking medical care, it is likely that the true rate of death among people infected with COVID-19 could be even lower. On the other hand, the estimated mortality rates reported in most studies need to be interpreted with caution as they are often based on the cumulative number of deaths relative to the number of confirmed cases, while patients who die on a given day have been infected at a much earlier date and this would bias the denominator of the mortality rate (38). We acknowledge four main limitations of our systematic review. First, our findings are mainly based on studies that recruited patient from clinics and hospitals and so may be biased toward more severe cases. Second, many studies did not report the study outcomes by subgroups such as age or gender and so we could report group-specific outcomes. Third, we used the mean and the standard error of the incubation period assuming a normal distribution which may have led to underestimate the right tail of the distribution. Lastly, given the urgency of topic and the heterogeneity of the studies included in the review, we did not conduct risk of bias and quality assessment of the studies. Inevitably, given the novelty of COVID-19 and the observational nature o...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.04.01.20050138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Adjusted Beta = -1.16; P-value = 0.239 ) or percent of male ( Adjusted Beta = -13.07; P-value = 0.09 ) participants . Table 2: Resources
Software and Algorithms Sentences Resources Data Sources: MEDLINE, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 without language restrictions as well as bibliographies of relevant articles. MEDLINEsuggested: (MEDLINE, SCR_002185)<div style="margin-bottom:8px"> <div><b>Embase</b></div> <div>suggested: …SciScore for 10.1101/2020.04.01.20050138: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Adjusted Beta = -1.16; P-value = 0.239 ) or percent of male ( Adjusted Beta = -13.07; P-value = 0.09 ) participants . Table 2: Resources
Software and Algorithms Sentences Resources Data Sources: MEDLINE, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 without language restrictions as well as bibliographies of relevant articles. MEDLINEsuggested: (MEDLINE, SCR_002185)<div style="margin-bottom:8px"> <div><b>Embase</b></div> <div>suggested: (EMBASE, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001650">SCR_001650</a>)</div> </div> <div style="margin-bottom:8px"> <div><b>Google Scholar</b></div> <div>suggested: (Google Scholar, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008878">SCR_008878</a>)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Databases and Search Strategy Following the Systematic Reviews and Meta-Analyses ( PRISMA ) checklist ( 16 ) and the Peer Review of Electronic Search Strategies ( PRESS ) guideline ( 17) , we searched PubMed , Embase , and Google Scholar from December 01 , 2019 through to March 11 , 2020 for studies that measured and reported several characteristics of COVID-19 ( e.g. , incubation period , hospitalization , death)</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div><b>PubMed</b></div> <div>suggested: (PubMed, <a href="https://scicrunch.org/resources/Any/search?q=SCR_004846">SCR_004846</a>)</div> </div> </td></tr></table>Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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