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http://dx.doi.org/10.5351/KJAS.2021.34.3.375

Mixed-effects zero-inflated Poisson regression for analyzing the spread of COVID-19 in Daejeon  

Kim, Gwanghee (Department of Information and Statistics, Chungnam National University)
Lee, Eunjee (Department of Information and Statistics, Chungnam National University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.3, 2021 , pp. 375-388 More about this Journal
Abstract
This paper aims to help prevent the spread of COVID-19 by analyzing confirmed cases of COVID-19 in Daejeon. A high volume of visitors, downtown areas, and psychological fatigue with prolonged social distancing were considered as risk factors associated with the spread of COVID-19. We considered the weekly confirmed cases in each administrative district as a response variable. Explanatory variables were the number of passengers getting off at a bus station in each administrative district and the elapsed time since the Korean government had imposed distancing in daily life. We employed a mixed-effects zero-inflated Poisson regression model because the number of cases was repeatedly measured with excess zero-count data. We conducted k-means clustering to identify three groups of administrative districts having different characteristics in terms of the number of bars, the population size, and the distance to the closest college. Considering that the number of confirmed cases might vary depending on districts' characteristics, the clustering information was incorporated as a categorical explanatory variable. We found that Covid-19 was more prevalent as population size increased and a district is downtown. As the number of passengers getting off at a downtown district increased, the confirmed cases significantly increased.
Keywords
COVID-19; zero-inflated Poisson model; mixed-effects; Daejeon;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Agresti A (2020). An Introduction to Categorical Data Analysis, Wiley.
2 Hall DB (2000). Zero-inflated poisson and binomial regression with random effects: a case study, Biometrics, 54, 1030-1039.   DOI
3 Kim JT, Lee NY, Oh MA, and Lee SI (2018). A study on the transition of population movement and centroid in Korea, Journal of The Korean Official Statistics, 23, 1-23.
4 Kim YK and Hwang BS (2018). A Bayesian zero-inflated Poisson regression model with random effects with application to smoking behavior, The Korean Journal of Applied Statistics, 31, 287-301.   DOI
5 Liu J, Yanyuan M, and Jill J (2020). A goodness-of-fit test for zero-inflated Poisson mixed effects models in tree abundance studies, Computational Statistics & Data Analysis, 144.
6 Moon, et al. (2020). Time-variant reproductive number of COVID-19 in Seoul, Korea, Epidemiology and Health, 42.
7 Min Y and Agresti A (2005). Random effect models for repeated measures of zero-inflated count data, Statistical Modelling, 5, 1-19.   DOI
8 Zhu H, Luo S, and Stacia MD (2015). Zero-inflated count models for longitudinal measurements with heterogeneous random effects, Statistical Methods in Medical Research, 26, 1774-1786.   DOI
9 Han JH and Kim CH (2015). Zero inflated Poisson model for spatial data, The Korean Journal of Applied Statistics, 28, 231-239.   DOI
10 Lambert D (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics, 34, 1-14.   DOI
11 Wang K, Yau KK, and Lee AH (2002). A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays, Computer Methods and Programs in Biomedicine, 68, 195-203.   DOI