On Dynamical Analysis of the Data-Driven SIR model (COVID-19 Outbreak in Indonesia)

  1. Albert Sulaiman

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

An archipelago country such as Indonesia has a different beginning of the outbreak, therefore the management of epidemics not uniform. For this reason, the results in the data of confirmed cases COVID-19 to fluctuate and difficult to predict. We use the data-driven SIR model to analyze the dynamics and behavior of the evolution of the disease. We run the data-driven SIR model gradually and found that there are shifting of the peak and the distance of saturation point. We found that a transmission acceleration of the outbreak occurring in Indonesia where it could be seen from increasing of the time the saturation and the confirmed cases. It is finally argued that a new parameter can be used to guidance the condition when the new normal begins.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.22.20137810: (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

    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.

  2. Version published to 10.1101/2020.06.22.20137810v1 on medRxiv