Predicting COVID-19 outbreak using open mobility data for minimal disruption on the country’s economy

  1. Héctor Morales-Fajardo
  2. Jorge Rodríguez-Arce

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Abstract

COVID-19 is an infectious disease caused by the SARS-COV-2 coronavirus, which was discovered in late 2019. Within a few months, COVID-19 was declared a global pandemic by the WHO. Several countries adopted social distancing measures, such as self-quarantine, workplace and mobility restrictions, reducing the probability of contact between non-infected and infected people. In general, these measures have a negative impact on low-income economies and small and medium businesses. During the outbreak, several predictive models have been proposed in order to assess the level of saturation that health services might have. Nevertheless, none of them considers information on the people’s mobility to assess the effectiveness of the social distancing policies. In this study, the authors propose a prediction method based on people’s open mobility data from Apple© and Google© databases to project potential scenarios and monitor case growth. The proposed method shows the importance of monitoring daily case increase for the first 4-6 weeks of the pandemic wave. Active monitoring is crucial to determine the reduction in mobility and proper actions. The results can contribute to health authorities for making timely decisions, preventing the spread of viruses while balancing the reduction of mobility with minimal disruption in people’s economies in future outbreaks.

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  1. ScreenIT

    SciScore for 10.1101/2022.02.12.22270892: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2022.02.12.22270892 on medRxiv