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Local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area, Korea: a view from a Bayesian perspective
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Youngbin Lym, Hyobin Lym, Ki-Jung Kim
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Epidemiol Health. 2022;44:e2022016. Published online January 13, 2022
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DOI: https://doi.org/10.4178/epih.e2022016
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Abstract
OBJECTIVES
The purpose of this study was to enhance the understanding of the local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area (GSA), Korea, after its initial outbreak in January 2020.
METHODS Using the weekly aggregates of coronavirus disease 2019 (COVID-19) cases of 77 municipalities in the GSA, we examined the relative risks of COVID-19 infection across local districts over 50 consecutive weeks in 2020. To this end, we employed a spatiotemporal generalized linear mixed model under the hierarchical Bayesian framework. This allowed us to empirically examine the random effects of spatial alignments, temporal autocorrelation, and spatiotemporal interaction, along with fixed effects. Specifically, we utilized the conditional autoregressive and the weakly informative penalized complexity priors for hyperparameters of the random effects.
RESULTS Spatiotemporal interaction dominated the overall variability of random influences, followed by spatial correlation, whereas the temporal correlation appeared to be small. Considering these findings, we present dynamic changes in the spread of COVID-19 across local municipalities in the GSA as well as regions at elevated risk for further policy intervention.
CONCLUSIONS The outcomes of this study can contribute to advancing our understanding of the local-level COVID-19 spread dynamics within densely populated regions in Korea throughout 2020 from a different perspective, and will contribute to the development of regional safety planning against infectious diseases.
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Summary
Korean summary
본 논문은 수도권 지역에서 코로나바이러스가 처음 발생한 2020년 1월 이후 12월 말까지 총 50주 동안 수도권 내 지자체 수준에서의 코로나 19 전염병에 대한 시공간적 확산 역동성을 파악하기 위한 연구이다. 데이터 기반의 실증분석을 위한 계층적 베이지언 기법 기반의 시공간 일반화 선형 혼합모형의 결과에 따르면, 확률효과 중 시공간적 상호작용의 영향성이 가장 크게 나타났고, 다음으로는 공간자기상관에 의한 영향 순으로 나타난 반면, 시간에 의한 확률효과는 상대적으로 적게 도출되었다. 연구의 결과를 종합하여, 본 연구에서는 지도 기반의 코로나 19 위험 및 그 위험의 시공간적 변화를 제시하고, 향후 전염병에 대한 정책대응에 활용될 수 있도록 하였다.
Key Message
This study investigates the local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area, Korea, after its initial outbreak in January 2020. We adopt a flexible hierarchical Bayesian method so as to account for latent influences of space and time along with the fixed effects by covariates. The results suggest that spatiotemporal interaction dominates the overall variability of random influences, followed by spatial correlation, while the temporal effect appears to be small. Based on these findings, we present maps that depict dynamic changes of COVID-19 infection as well as regions of elevated risks for further policy consideration.
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Citations
Citations to this article as recorded by  - A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology
Yufeng Wang, Xue Chen, Feng Xue
ISPRS International Journal of Geo-Information.2024; 13(3): 97. CrossRef
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