Local level spatiotemporal dynamics of the COVID-19 transmission in the Greater Seoul Area: a view from a Bayesian perspective

Lym, Youngbin; Lym, Hyobin; Kim, Ki-Jung

doi:2022.0.0.e2022016

Original article

Epidemiology and Health 2022;e2022016.
DOI: https://doi.org/10.4178/epih.e2022016 [Accepted] Published online Jan 13, 2022.

Local level spatiotemporal dynamics of the COVID-19 transmission in the Greater Seoul Area: a view from a Bayesian perspective

Youngbin Lym¹

, Hyobin Lym²

, Ki-Jung Kim³

¹Research Institute of Natural Sciences, Chungnam National University, Daejeon , Korea
²Center for Agricultural Outlook Sejong Office, Korea Rural Economic Institute, Cheongju-si, Korea
³Department of Smart Car Engineering, Doowon Technical University, Paju-si, Korea

Correspondence Ki-Jung Kim ,Email: kimkj@doowon.ac.kr

Received: Nov 10, 2021 Accepted after revision: Jan 13, 2022

Abstract

Objectives:
The purpose of this research is to enhance the understanding of the local-level spatiotemporal dynamics of COVID-19 diffusion in the Greater Seoul Area (GSA), South Korea, after its initial outbreak in January 2020.
Method:
Using the weekly aggregates of COVID-19 cases of 77 municipalities in the GSA, we attempt to examine the relative risks of COVID-19 infections across local districts over the fifty-consecutive weeks in 2020. To this end, we employ a spatiotemporal generalized linear mixed model under the hierarchical Bayesian framework. This allows us to empirically examine the random effects of spatial alignments, temporal autocorrelation, and spatiotemporal interaction along with the fixed effects. Specifically, we exploit the intrinsic conditional autoregressive and the weakly informative penalized complexity priors for hyperparameters of the random effects.
Results:
Our study reveals that spatiotemporal interaction dominates the overall variability of random influences, followed by spatial correlation, whereas the temporal correlation appears to be small. Considering these findings, we present the dynamic evolution of COVID-19 across local municipalities in the GSA as well as regions of an 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 South Korea throughout 2020 from a different perspective, leading to devising regional safety planning against infectious diseases.

Keywords: COVID-19; Hierarchical Bayesian; Spatiotemporal transmission dynamics; Local municipalities; Relative risks