Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia.
Int J Environ Res Public Health
; 19(11)2022 05 30.
Article
in English
| MEDLINE | ID: covidwho-1869616
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Cell Phone
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
Ijerph19116671
Similar
MEDLINE
...
LILACS
LIS