Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

  1. Ratih Oktri Nanda
  2. Aldilas Achmad Nursetyo
  3. Aditya Lia Ramadona
  4. Muhammad Ali Imron
  5. Anis Fuad
  6. Althaf Setyawan
  7. Riris Andono Ahmad

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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.

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  1. Version published to 10.3390/ijerph19116671
  2. ScreenIT

    SciScore for 10.1101/2021.07.24.21261016: (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
    The descriptions of the places are as follows: We applied statistical transformations to our data and used STATA software 14.0 to analyze the data.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Additionally, some confounding and other possible limitations might exist during the data exploration. Therefore, the result in this study should be interpreted with caution. Mobility on groceries and pharmacies contributed the highest in the COVID-19 new cases increase. Food warehouses, farmers’ markets, specialty markets, and drugstores were among the essential businesses allowed to be opened during the restrictions. This finding is in line with a previous study in India, where travel for daily needs purposes are related to COVID-19 transmission.[14] A previous study observed that people shifted from restaurants towards groceries and food sellers during the stay-at-home orders [15]. We assumed that markets, mainly traditional markets, were places with frequent visitors in Jakarta as those places supplied daily necessities for the people. However, traditional markets tend to be crowded and unorganized, also challenging for social distancing implementation. Though the government ruled out health protocols in traditional markets, it was not strongly enforced. Around 107 traditional market clusters and 555 cases were reported in Jakarta, only in the first relaxation period. 22 Precisely, traditional markets contributed 4.3% of increased COVID-19 new cases in Jakarta [16]. Places like café, restaurants, shopping malls, recreation areas, and parks in Jakarta were shut down in March-June and subsequently reopened, adjusting around 50% of the total capacity during the first relaxat...

    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.

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  3. Version published to 10.1101/2021.07.24.21261016v1 on medRxiv