Community Mobility and COVID-19 Dynamics in Jakarta, 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.

Covid-19 Effect on Community Mobility and Stock Market of Malaysia

“…The study found that Retail and Recreation have low mobility, but the Grocery, Pharmacy and Transit Stations have higher mobility. In our neighbouring country, Indonesia, the Grocery and Pharmacy is reported to be the highest (4.12%) in predicting COVID-19 dynamics (Nanda R.O. 2022).…”

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

“…Nevertheless, interest in many works has focused on the impact of COVID-19 on mobility, and few efforts have been directed at assessing how regulating mobility rates can have positive effects on controlling virus transmission. Examples of these studies have focused on Taiwan [ 6 ], Poland [ 7 ], the United States [ 8 ], India [ 9 ], Spain [ 10 , 11 ], Japan [ 12 ], the United Arab Emirates [ 13 ], Saudi Arabia [ 14 ], Greece [ 15 ], China [ 16 , 17 ], Indonesia [ 18 ], Austria [ 19 ], Italy [ 20 ], Portugal [ 21 , 22 ], South Africa [ 23 ], Costa Rica [ 24 ], and Australia [ 25 ], among others.…”

STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information

“…Cooper et al [7] proposed a new model based on the SIR model, which is able to dynamically track changes in community infection. However, traditional prediction models consider fewer factors, and the spread of COVID-19 is influenced by several factors, such as population movement [8] , virus variants [9] , and city size [10] , which can affect the ability of COVID-19 to spread. Therefore, more advanced methods should be used in order to predict COVID-19 more effectively.…”