Mobility data can explain the entire COVID-19 outbreak course in Japan

  1. Junko Kurita
  2. Tamie Sugawara
  3. Yasushi Ohkusa

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Abstract

Background

In Japan, as a countermeasure against the COVID-19 outbreak, voluntary restrictions against going out (VRG) have been applied.

Object

We examined mobility information provided by Apple Inc. to a susceptible–infected–recovery model.

Method

When applying a polynomial function to daily Apple data with the SIR model, we presumed the function up to a cubic term as in our earlier study.

Results

Estimation results demonstrated R 0 as 1.507 and its 95% confidence interval was [1.502, 1.509].. The estimated coefficients of Apple data was 1.748 and its 95% confidence interval was [1.731, 1.788].

Discussion and Conclusion

Results show that mobility data from Apple Inc. can explain the entire course of the outbreak in COVID-19 in Japan. Therefore, monitoring Apple data might be sufficient to adjust control measures to maintain an effective reproduction number of less than one.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.26.20081315: (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

    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:
    The present study has some limitations. First, we examined the explanatory power for the COVID-19 outbreak nationwide, but is it applicable at the prefecture level? For example, did Apple data for Osaka prefecture have sufficient explanatory power to assess the outbreak dynamics? If so, one must monitor Apple data from Osaka or other prefectures carefully. Similarly, are these data and methods useful for countries other than Japan? If these methods hold in the US, for example, Apple data might contribute to efforts at controlling outbreaks there. Secondly, one must be reminded that Apple data show a proportion of users leaving their residence. The data do not directly indicate a number of contacts or even a rate of contact. In other words, Apple data reflect no intensity of the respective contacts. However, such measurement of contact intensity is extremely difficult. That measurement represents a future research objective. Thirdly, although Apple data was better than others, the users of Apple products and services might be limited to young or healthy persons. By contrast, information from NTT or JR might reflect the activities of an otherwise limited scope or number of users. Therefore, a combination of these data with Apple data might be better than the data used for the present study. That is anticipated as our next challenge.

    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 10.1101/2020.04.26.20081315 on medRxiv