The combination of factors influencing COVID-19 infections in Korea, by region: Performing neural network analysis and qualitative comparative analysis

Min-hyuk Choi; Nemoto Masatsugu

doi:10.53730/ijhs.v6nS2.8527

Authors

Min-hyuk Choi
jesmondmh@naver.com
MPH Student, Mailman School of Public Health, Columbia University, USA
Nemoto Masatsugu Research Professor, IDI, Chungbuk National University, Korea

Keywords:

corona in Korea, neural network analysis, QCA, corona infection rate, combination condition

Abstract

Given the fact that the number of COVID-19 infections in Korea shows a large variation by region, this study attempted to analyse what combination of factors might lead to a decrease in the number of infections. As the dependent variable, the proportion of the local population infected with the virus (i.e. the coronavirus infection rate) was used. Sixteen independent variables were then selected, and five of them which have an important effect were selected from among these. Neural network analysis and Qualitative Comparative Analysis (QCA) were the analysis methods used. In selecting the analysis period, from January 2020 to December 2021, it was found that COVID-19 in Korea could be divided into three stages, each of which could be explained by a different combination of conditions.

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