Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID‐19: Meta‐analysis and sensitivity analysis

  1. Wenqing He
  2. Grace Y. Yi
  3. Yayuan Zhu

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Abstract

The coronavirus disease‐2019 (COVID‐19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). However, comprehensive knowledge of COVID‐19 remains incomplete and many important features are still unknown. This manuscript conducts a meta‐analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41‐3.90), the average incubation time to be 5.08 days with the 95% CI (4.77‐5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%‐73.60%), and the case fatality rate to be 2.72% with 95% CI (1.29%‐4.16%) where asymptomatic infections are accounted for.

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  1. Version published to 10.1002/jmv.26041
  2. ScreenIT

    SciScore for 10.1101/2020.04.28.20083758: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 Search Strategy and Selection Criteria: The third author (Y.Z.) conducted a literature screening for the articles published between January 24, 2020 and March 31, 2020 by using online databases, including PubMed, Web of Science, Google Scholar and the official websites of core scientific and biomedical journals including Science, Nature, The Lancet, The New England Journal of Medicine, and The Journal of American Medical Association, as well as some preprint platforms such as BioRxiv and MedRxir, with search terms specified as COVID-19, SARS-CoV-2, 2019-nCov, and novel coronavirus.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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:
    The present investigations have limitations. Not all published results for the four measures are included in our study; we do not include those manuscripts which reported merely a point estimate without the associated standard deviation or a 95% confidence intervals, because they do not allow us to decide a proper weight for the inclusion of the result. While reporting a single estimate of the average incubation time and the case fatality rate gives us an easy way to assess the impact of COVID-19, such measures marginalize the effects from the associated factors such as the disease severity, the patient’s medical conditions, and age. With more studies available for categorizing the case fatality rate or the incubation time, it is useful to apply the meta-analysis to estimate those measures by stratifying the population based on the demographic and clinical characteristics. When data at the individual level are available, better estimates of key features for COVID-19 can be obtained and the pandemic trend can be more reasonably projected using statistical regression models.

    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.
    • Thank you for including a protocol registration statement.

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.28.20083758v1 on medRxiv