Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models
COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2023-05-01
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| Series: | Geospatial Health |
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| Online Access: | https://geospatialhealth.net/index.php/gh/article/view/1161 |
| _version_ | 1827941174728458240 |
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| author | I Gede Nyoman Mindra Jaya Anna Chadidjah Farah Kristiani Gumgum Darmawan Jane Christine Princidy |
| author_facet | I Gede Nyoman Mindra Jaya Anna Chadidjah Farah Kristiani Gumgum Darmawan Jane Christine Princidy |
| author_sort | I Gede Nyoman Mindra Jaya |
| collection | DOAJ |
| description |
COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.
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| first_indexed | 2024-03-13T09:33:18Z |
| format | Article |
| id | doaj.art-4eeeecf446e34fa88c18eab330310942 |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-03-13T09:33:18Z |
| publishDate | 2023-05-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-4eeeecf446e34fa88c18eab3303109422023-05-25T18:21:16ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962023-05-0118110.4081/gh.2023.1161Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient modelsI Gede Nyoman Mindra Jaya0Anna Chadidjah1Farah Kristiani2Gumgum Darmawan3Jane Christine Princidy4Statistics Department, Universitas Padjadjaran, BandungStatistics Department, Universitas Padjadjaran, BandungMathematics Department, Parahyangan Catholic University, BandungStatistics Department, Universitas Padjadjaran, BandungStatistics Department, Universitas Padjadjaran, Bandung COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted. https://geospatialhealth.net/index.php/gh/article/view/1161COVID-19human mobilityspatial and temporal autocorrelationsFacebook mobility dataIndonesia |
| spellingShingle | I Gede Nyoman Mindra Jaya Anna Chadidjah Farah Kristiani Gumgum Darmawan Jane Christine Princidy Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models Geospatial Health COVID-19 human mobility spatial and temporal autocorrelations Facebook mobility data Indonesia |
| title | Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models |
| title_full | Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models |
| title_fullStr | Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models |
| title_full_unstemmed | Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models |
| title_short | Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models |
| title_sort | does mobility restriction significantly control infectious disease transmission accounting for non stationarity in the impact of covid 19 based on bayesian spatially varying coefficient models |
| topic | COVID-19 human mobility spatial and temporal autocorrelations Facebook mobility data Indonesia |
| url | https://geospatialhealth.net/index.php/gh/article/view/1161 |
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