Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature

Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee

Research output: Contribution to journalReview articlepeer-review


South Korea implemented interventions to curb the spread of the novel coronavirus disease 2019 (COVID-19) pandemic with discovery of the first case in early 2020. Mathematical modeling designed to reflect the dynamics of disease transmission has been shown to be an important tool for responding to COVID-19. This study aimed to review publications on the structure, meth-od, and role of mathematical models focusing on COVID-19 transmission dynamics in Korea. In total, 42 papers published between August 7, 2020 and August 21, 2022 were studied and reviewed. This study highlights the construction and utilization of mathematical models to help craft strategies for predicting the course of an epidemic and evaluating the effectiveness of control strategies. Despite the limitations caused by a lack of available epidemiological and surveillance data, modeling studies could contribute to providing scientific evidence for policymaking by simulating various scenarios.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalYonsei medical journal
Issue number1
Publication statusPublished - 2023 Jan

Bibliographical note

Funding Information:
The work of Jeehyun Lee was supported by a government-wide R&D Fund project for infectious disease research through GFID, funded by seven ministries (grant number: HG20C0003) and NRF-2020R1A2C1A01010775. Hyojung Lee was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grant number: NRF-2022R1 A2C3011711, NRF-2022R1A5A1033624). This research was supported by a fund(2022-03-008) by Research of Korea Disease Control and Prevention Agency. The authors thank Taeyong Lee for his valuable comments on the conceptualization and literature search.

Publisher Copyright:
© Yonsei University College of Medicine 2023.

All Science Journal Classification (ASJC) codes

  • Medicine(all)


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