The relationship between new PCR positive cases and going out in public during the COVID-19 epidemic in Japan

  1. Hiromichi Takahashi
  2. Iori Terada
  3. Takuya Higuchi
  4. Daisuke Takada
  5. Jung-ho Shin
  6. Susumu Kunisawa
  7. Yuichi Imanaka

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Abstract

The suppression of the first wave of COVID-19 in Japan is assumedly attributed to people’s increased risk perception after acquiring information from the government and media reports. In this study, going out in public amidst the spread of COVID-19 infections was investigated by examining new polymerase chain reaction (PCR) positive cases of COVID-19 and its relationship to four indicators of people going out in public (the people flow, the index of web searches for going outside, the number of times people browse restaurants, and the number of hotel guests, from the Regional Economic and Social Analysis System (V-RESAS). Two waves of COVID-19 infections were examined using cross-correlation analysis. In the first wave, all four indicators of going out changed to be opposite the change in new PCR positive cases, showing a lag period of –1 to +6 weeks. In the second wave, the same relationship was only observed for the index of web searches for going outside, and two indicators showed the positive lag period of +6 to +12 weeks after the change in new PCR positive cases. Moreover, each indicator in the second wave changed differently compared to the first wave. The complexity of people’s behaviors around going out increased in the second wave, when policies and campaigns were implemented and people’s attitudes were thought to have changed. In conclusion, the results suggest that policies may have influenced people’s mobility, rather than the number of new PCR positive cases.

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  1. Version published to 10.1371/journal.pone.0266342
  2. ScreenIT

    SciScore for 10.1101/2021.03.07.21252959: (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: We detected the following sentences addressing limitations in the study:
    Limitations: The data were sourced from the V-RESAS website. Since the data was from private entities, it does not include data from all people. However, the data used in this study is also used by policymakers and the media, so it is considered to have some validity [42]. There might be seasonal variations in people’s behavioral changes. Since the analysis period was from January 16 to the first week of November, the results might have been underestimated or overestimated because of seasonal variations that might not have been considered in the analysis. However, since people’s behavior during a pandemic differed significantly from a usual year, this limitation is not considered a serious problem.

    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 10.1101/2021.03.07.21252959v1 on medRxiv