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Evolution of Responses to COVID-19 and Epidemiological Characteristics in South Korea

Author

Listed:
  • Junhwi Jeon

    (Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Korea
    These authors contributed equally to this work.)

  • Changyong Han

    (Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Korea
    These authors contributed equally to this work.)

  • Tobhin Kim

    (Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Korea)

  • Sunmi Lee

    (Department of Applied Mathematics, Kyung Hee University, Yongin 17104, Korea)

Abstract

The characteristics of COVID-19 have evolved at an accelerated rate over the last two years since the first SARS-CoV-2 case was discovered in December 2019. This evolution is due to the complex interplay among virus, humans, vaccines, and environments, which makes the elucidation of the clinical and epidemiological characteristics of COVID-19 essential to assess ongoing policy responses. In this study, we carry out an extensive retrospective analysis on infection clusters of COVID-19 in South Korea from January 2020 to September 2021 and uncover important clinical and social factors associated with age and regional patterns through the sophisticated large-scale epidemiological investigation using the data provided by the Korea Disease Control and Prevention Agency (KDCA). Epidemiological data of COVID-19 include daily confirmed cases, gender, age, city of residence, date of symptom onset, date of diagnosis, and route of infection. We divide the time span into six major periods based on the characteristics of COVID-19 according to various events such as the rise of new variants, vaccine rollout, change of social distancing levels, and other intervention measures. We explore key features of COVID-19 such as the relationship among unlinked, asymptomatic, and confirmed cases, serial intervals, infector–infectee interactions, and age/region-specific variations. Our results highlight the significant impact of temporal evolution of interventions implemented in South Korea on the characteristics of COVID-19 transmission, in particular, that of a high level of vaccination coverage in the senior-aged group on the dramatic reduction of confirmed cases.

Suggested Citation

  • Junhwi Jeon & Changyong Han & Tobhin Kim & Sunmi Lee, 2022. "Evolution of Responses to COVID-19 and Epidemiological Characteristics in South Korea," IJERPH, MDPI, vol. 19(7), pages 1-20, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:4056-:d:782275
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    References listed on IDEAS

    as
    1. Hyojung Lee & Changyong Han & Jooyi Jung & Sunmi Lee, 2021. "Analysis of Superspreading Potential from Transmission Clusters of COVID-19 in South Korea," IJERPH, MDPI, vol. 18(24), pages 1-13, December.
    2. Giovanni Gabutti & Erica d’Anchera & Francesco De Motoli & Marta Savio & Armando Stefanati, 2021. "The Epidemiological Characteristics of the COVID-19 Pandemic in Europe: Focus on Italy," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    3. Yongin Choi & James Slghee Kim & Jung Eun Kim & Heejin Choi & Chang Hyeong Lee, 2021. "Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach," IJERPH, MDPI, vol. 18(8), pages 1-19, April.
    4. Min Xu & Chunxiang Cao & Xin Zhang & Hui Lin & Zhong Yao & Shaobo Zhong & Zhibin Huang & Robert Shea Duerler, 2021. "Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis," IJERPH, MDPI, vol. 18(7), pages 1-17, March.
    5. Elisabetta Cameroni & John E. Bowen & Laura E. Rosen & Christian Saliba & Samantha K. Zepeda & Katja Culap & Dora Pinto & Laura A. VanBlargan & Anna Marco & Julia Iulio & Fabrizia Zatta & Hannah Kaise, 2022. "Broadly neutralizing antibodies overcome SARS-CoV-2 Omicron antigenic shift," Nature, Nature, vol. 602(7898), pages 664-670, February.
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    Cited by:

    1. Lee, Hyosun & Abdulali, Arsen & Park, Haeyoung & Lee, Sunmi, 2024. "Optimal region-specific social distancing strategies in a complex multi-patch model through reinforcement learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 226(C), pages 24-41.

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