Estimation of novel coronavirus ( COVID ‐19) reproduction number and case fatality rate: A systematic review and meta‐analysis

  1. Tanvir Ahammed
  2. Aniqua Anjum
  3. Mohammad Meshbahur Rahman
  4. Najmul Haider
  5. Richard Kock
  6. Md Jamal Uddin

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Abstract

Background and aims

Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID‐19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number ( R 0 ) and case fatality rate (CFR) of COVID‐19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID‐19 and assess whether the parameters vary in different regions of the world.

Methods

We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random‐effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used.

Results

Forty‐five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world.

Conclusions

An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID‐19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

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  1. Version published to 10.1002/hsr2.274
  2. ScreenIT

    SciScore for 10.1101/2020.09.30.20204644: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Data screening and extraction: After excluding duplicate papers, two investigators (TA and AA) independently screened the titles and abstracts using the eligibility criteria and then assessed the rest full-text articles for eligibility.
    TA
    suggested: RRID:CVCL_4315)
    Software and Algorithms
    SentencesResources
    Search strategy: We systematically searched the major databases: LitCovid (a curated COVID-19 database of PubMed), PubMed, EMBASE, Scopus, and Web of Science databases from 1 January 2020 to 31 August 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Records were managed by Mendeley version 1.19.4 software to exclude duplicates.
    Mendeley
    suggested: (Mendeley Data, RRID:SCR_002750)

    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:
    Limitations: Some of the included studies were limited in terms of sample size, missing information on the onset of COVID-19, and data availability. Therefore, the reported findings should be interpreted cautiously within that context. Furthermore, our study was limited to the articles published in the English language only. Considering the epicenter of COVID-19, Chinese literature should be included in future systematic reviews. Another important limitation is the case detection and testing heterogeneity. Countries’ responses varied, and some countries are facing difficulties in testing enough samples. Further, the quality of the testing samples also varies in different countries. All these have affected the case detection and thus impacted the R0 and CFR. However, we believe the effects are minimum, and data across the world are still comparable.

    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.

    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 10.1101/2020.09.30.20204644v1 on medRxiv