Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea
This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices al...
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| Veröffentlicht in: | International journal of environmental research and public health 2022-01, Vol.19 (2), p.824 |
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| creator | Lym, Youngbin Lym, Hyobin Kim, Keekwang Kim, Ki-Jung |
| description | This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases. |
| doi_str_mv | 10.3390/ijerph19020824 |
| format | Article |
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To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph19020824</identifier><identifier>PMID: 35055645</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Bayes Theorem ; Bayesian analysis ; Coronaviruses ; COVID-19 ; Decision making ; Disease spread ; Disease transmission ; Fatalities ; Growth models ; Health risks ; Heterogeneity ; Humans ; Infections ; Infectious diseases ; Municipalities ; Pandemics ; Population ; Population density ; Provinces ; Public health ; Regions ; Republic of Korea - epidemiology ; Risk ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; Sociodemographics ; Spatial data ; Traffic accidents & safety</subject><ispartof>International journal of environmental research and public health, 2022-01, Vol.19 (2), p.824</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Lym, Hyobin ; Kim, Keekwang ; Kim, Ki-Jung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-a50424b97dcb6c575c6d5bfc9e6e0be5bd9ae5f760f1ceeae9c74e37cd6afe6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Decision making</topic><topic>Disease spread</topic><topic>Disease transmission</topic><topic>Fatalities</topic><topic>Growth models</topic><topic>Health risks</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Municipalities</topic><topic>Pandemics</topic><topic>Population</topic><topic>Population density</topic><topic>Provinces</topic><topic>Public health</topic><topic>Regions</topic><topic>Republic of Korea - epidemiology</topic><topic>Risk</topic><topic>SARS-CoV-2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Sociodemographics</topic><topic>Spatial data</topic><topic>Traffic accidents & safety</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lym, Youngbin</creatorcontrib><creatorcontrib>Lym, Hyobin</creatorcontrib><creatorcontrib>Kim, Keekwang</creatorcontrib><creatorcontrib>Kim, Ki-Jung</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lym, Youngbin</au><au>Lym, Hyobin</au><au>Kim, Keekwang</au><au>Kim, Ki-Jung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2022-01-12</date><risdate>2022</risdate><volume>19</volume><issue>2</issue><spage>824</spage><pages>824-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). 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| ispartof | International journal of environmental research and public health, 2022-01, Vol.19 (2), p.824 |
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| subjects | Bayes Theorem Bayesian analysis Coronaviruses COVID-19 Decision making Disease spread Disease transmission Fatalities Growth models Health risks Heterogeneity Humans Infections Infectious diseases Municipalities Pandemics Population Population density Provinces Public health Regions Republic of Korea - epidemiology Risk SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Sociodemographics Spatial data Traffic accidents & safety |
| title | Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea |
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