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Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

Author

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  • Ratih Oktri Nanda

    (Center for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Aldilas Achmad Nursetyo

    (Center for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Aditya Lia Ramadona

    (Department of Health Behavior, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Muhammad Ali Imron

    (Wildlife Laboratory, Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Anis Fuad

    (Center for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
    Department of Biostatistics, Epidemiology, Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Althaf Setyawan

    (Department of Reproductive Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Riris Andono Ahmad

    (Center for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
    Department of Biostatistics, Epidemiology, Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

Abstract

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA) . Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in “grocery and pharmacy” (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Suggested Citation

  • Ratih Oktri Nanda & Aldilas Achmad Nursetyo & Aditya Lia Ramadona & Muhammad Ali Imron & Anis Fuad & Althaf Setyawan & Riris Andono Ahmad, 2022. "Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6671-:d:827792
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    References listed on IDEAS

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