Forecasting undetected COVID-19 cases in Small Island Developing States using Bayesian approach

  1. Andrio Adwibowo

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Abstract

In dealing with the COVID-19, the fundamental question is how many actually undetected cases are going around regarding the capabilities of current health systems to contain the virus?. Due to a large number of asymptomatic cases, most COVID-19 cases are possibly undetected. For that reason, this study aims to provide an efficient, versatile, easy to compute, and robust estimator for the number of undetected cases using Bayes theorem based on the actual COVID-19 cases. This theorem is applied to 25 Small Island Developing States (SIDS) due to SIDS vulnerability. The results in this study forecast that possibly undetected COVID-19 cases are approximately 4 times larger than the numbers of actual COVID-19 cases as observed. This finding highlights the importance of using modeling tool to get the better and comprehensive of current COVID-19 cases and to take immediately precaution approaches to mitigate the growing numbers of COVID-19 cases as well.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.13.20100545: (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

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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

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  2. Version published to 10.1101/2020.05.13.20100545v1 on medRxiv