Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore
Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223002005 |
| _version_ | 1797802886093078528 |
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| author | Wei Chien Benny Chin Chen-Chieh Feng Chan-Hoong Leong Junxiong Pang Hannah Eleanor Clapham Atsushi Nara Ming-Hsiang Tsou Yi-Chen Wang |
| author_facet | Wei Chien Benny Chin Chen-Chieh Feng Chan-Hoong Leong Junxiong Pang Hannah Eleanor Clapham Atsushi Nara Ming-Hsiang Tsou Yi-Chen Wang |
| author_sort | Wei Chien Benny Chin |
| collection | DOAJ |
| description | Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead to the transition of vulnerability from locations to locations. This study presented an analytical framework based on the Particle Swarm Optimization model to estimate the weights of the factors for vulnerability modeling, and a local proportional parameter for use in the integration of the local and neighboring area risks. A country model and five cross-region validation models were developed for the case study of Singapore to assess the vulnerability to COVID-19. The results showed that the identified weights for the factors were robust throughout the optimization process and across various models. The local proportional parameter could be set slightly higher in between 0.6 and 0.8 (out of 1), signifying that the local effect was higher than the neighboring effect. Computation of the weights from the optimal solutions for the integrated vulnerability index showed that the factors of human activity intensity and accessibility to amenities had much higher weights, at 0.5 and 0.3, respectively. Conversely, the weights of population density, elderly population, social economic status and land use diversity were much lower. These findings underscored the importance of considering non-equal weights for factors and incorporating spatial interactions between local and neighboring areas for vulnerability modeling, to provide to a more comprehensive assessment of vulnerability to infectious diseases. |
| first_indexed | 2024-03-13T05:12:30Z |
| format | Article |
| id | doaj.art-e6bed4b453b14e1d860822a4c4259981 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-13T05:12:30Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-e6bed4b453b14e1d860822a4c42599812023-06-16T05:09:00ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-07-01121103376Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in SingaporeWei Chien Benny Chin0Chen-Chieh Feng1Chan-Hoong Leong2Junxiong Pang3Hannah Eleanor Clapham4Atsushi Nara5Ming-Hsiang Tsou6Yi-Chen Wang7Department of Geography, National University of Singapore, 117568 Singapore, SingaporeDepartment of Geography, National University of Singapore, 117568 Singapore, SingaporeKantar Public, 228242 Singapore, SingaporeSinghealth Duke-NUS Global Health Institute and Center for Outbreak Preparedness, Duke-NUS Medical School, National University of Singapore, 169857 Singapore, SingaporeSaw Swee Hock School of Public Health, National University of Singapore, 117549 Singapore, SingaporeDepartment of Geography and Center for Human Dynamics in the Mobile Age, San Diego State University, San Diego, CA 92182, USADepartment of Geography and Center for Human Dynamics in the Mobile Age, San Diego State University, San Diego, CA 92182, USADepartment of Geography, National University of Singapore, 117568 Singapore, Singapore; Corresponding author.Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead to the transition of vulnerability from locations to locations. This study presented an analytical framework based on the Particle Swarm Optimization model to estimate the weights of the factors for vulnerability modeling, and a local proportional parameter for use in the integration of the local and neighboring area risks. A country model and five cross-region validation models were developed for the case study of Singapore to assess the vulnerability to COVID-19. The results showed that the identified weights for the factors were robust throughout the optimization process and across various models. The local proportional parameter could be set slightly higher in between 0.6 and 0.8 (out of 1), signifying that the local effect was higher than the neighboring effect. Computation of the weights from the optimal solutions for the integrated vulnerability index showed that the factors of human activity intensity and accessibility to amenities had much higher weights, at 0.5 and 0.3, respectively. Conversely, the weights of population density, elderly population, social economic status and land use diversity were much lower. These findings underscored the importance of considering non-equal weights for factors and incorporating spatial interactions between local and neighboring areas for vulnerability modeling, to provide to a more comprehensive assessment of vulnerability to infectious diseases.http://www.sciencedirect.com/science/article/pii/S1569843223002005VulnerabilityDisease riskSpatial big dataParticle Swarm OptimizationHuman dynamics |
| spellingShingle | Wei Chien Benny Chin Chen-Chieh Feng Chan-Hoong Leong Junxiong Pang Hannah Eleanor Clapham Atsushi Nara Ming-Hsiang Tsou Yi-Chen Wang Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore International Journal of Applied Earth Observations and Geoinformation Vulnerability Disease risk Spatial big data Particle Swarm Optimization Human dynamics |
| title | Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore |
| title_full | Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore |
| title_fullStr | Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore |
| title_full_unstemmed | Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore |
| title_short | Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore |
| title_sort | integrating local and neighboring area influences into vulnerability modeling of infectious diseases in singapore |
| topic | Vulnerability Disease risk Spatial big data Particle Swarm Optimization Human dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223002005 |
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