The effect of non-pharmaceutical interventions (NPIs) on the spread of COVID-19 pandemic in Japan: A modeling study

  1. Yingying Sun
  2. Jikai Sun

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Non-pharmaceutical interventions (NPIs) are founded to be effective to delay epidemic spread and to reduce the number of patients. Moderate NPIs took in Japan seemed to have reduced the COVID-19 patients and to lower death rates, thus, effects of those NPIs are worthy of investigation. We used open source data and divided the data into three periods: Jan 22 to Feb 25 (Period I), Feb 26 to Apr 6 (Period II), and Apr 7 to May 14 (Period III). We developed the SIRD model and applied the Monte Carlo Simulation to estimate a combination of optimal results, including the peak of infected cases, the peak date, and R 0 . For Period I, the estimated peak infected cases were smaller than the observed ones, the peak date was earlier than the observed one, and the R 0 was about 4.66. For the other two periods, the estimated cases were more, and the peak dates were earlier than the observed ones. The R 0 was 2.50 in Period II, and 1.79 in Period III. NPIs took in Japan might have reduced more than 50% of the daily contacts per people compared to that before COVID-19. Owing to the effects of NPIs, the Japanese society had avoided collapse of medical service. Nevertheless, the capacity of daily RT-PCR may have restricted the reported confirmed cases.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.22.20109660: (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

    Software and Algorithms
    SentencesResources
    We wrote the code package and run the simulation by Python 3.8.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.05.22.20109660v1 on medRxiv