Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia Article Swipe
Ratih Oktri Nanda
,
Aldilas Achmad Nursetyo
,
Aditya Lia Ramadona
,
Muhammad Ali Imron
,
Anis Fuad
,
Althaf Setyawan
,
Riris Andono Ahmad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph19116671
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/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.
Related Topics
Concepts
Poisson regression
Negative binomial distribution
Multicollinearity
Statistics
Count data
Econometrics
Generalized linear model
Linear regression
Regression analysis
Population
Coronavirus disease 2019 (COVID-19)
Poisson distribution
Pandemic
Principal component analysis
Variance inflation factor
Geography
Mathematics
Medicine
Environmental health
Disease
Infectious disease (medical specialty)
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph19116671
- https://www.mdpi.com/1660-4601/19/11/6671/pdf?version=1653907246
- OA Status
- gold
- Cited By
- 20
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282036145
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4282036145Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijerph19116671Digital Object Identifier
- Title
-
Community Mobility and COVID-19 Dynamics in Jakarta, IndonesiaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-30Full publication date if available
- Authors
-
Ratih Oktri Nanda, Aldilas Achmad Nursetyo, Aditya Lia Ramadona, Muhammad Ali Imron, Anis Fuad, Althaf Setyawan, Riris Andono AhmadList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph19116671Publisher landing page
- PDF URL
-
https://www.mdpi.com/1660-4601/19/11/6671/pdf?version=1653907246Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1660-4601/19/11/6671/pdf?version=1653907246Direct OA link when available
- Concepts
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Poisson regression, Negative binomial distribution, Multicollinearity, Statistics, Count data, Econometrics, Generalized linear model, Linear regression, Regression analysis, Population, Coronavirus disease 2019 (COVID-19), Poisson distribution, Pandemic, Principal component analysis, Variance inflation factor, Geography, Mathematics, Medicine, Environmental health, Disease, Infectious disease (medical specialty), PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 7, 2023: 6, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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