Center for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
Department of Health Behavior, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
Int J Environ Res Public Health. 2022 May 30;19(11):6671. doi: 10.3390/ijerph19116671.
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.
针对 COVID-19 大流行，人口流动的移动电话数据变得公开可用，包括谷歌社区流动性报告（CMR）。本研究探讨了利用流动数据来预测印度尼西亚雅加达的 COVID-19 动态。我们从 2020 年 2 月 15 日至 12 月 31 日获取了汇总和匿名的流动数据集。探索了三种统计模型：泊松回归广义线性模型（GLM）、负二项式回归 GLM 和多元线性回归（MLR）。由于存在多重共线性，使用主成分分析（PCA）将三个类别减少为一个单一指数。使用 PCA 进行变量调整的多元线性回归是最佳拟合模型，解释了雅加达 52%的 COVID-19 病例（R 平方：0.52；p<0.05）。本研究发现，不同类型的流动性是 COVID-19 病例的重要预测指标，对雅加达 COVID-19 动态的影响程度不同，其中“杂货店和药店”（4.12%）的影响最大。本研究表明，使用 CMR 数据有助于决策者做出决策和制定政策，尤其是在数据有限的情况下，并且可以通过预测病例激增来提高卫生系统的准备能力，例如在具有高传播风险的地方和季节性事件期间。
