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Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach

Author

Listed:
  • Yongin Choi

    (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
    These authors contributed equally to this work.)

  • James Slghee Kim

    (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
    These authors contributed equally to this work.)

  • Jung Eun Kim

    (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

  • Heejin Choi

    (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

  • Chang Hyeong Lee

    (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

Abstract

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age. Thus, when the vaccination supply is limited, age-dependent vaccination priority strategy should be implemented to minimize the incidences and mortalities. In this study, we developed an age-structured model for describing the transmission dynamics of COVID-19, including vaccination. Using the model and actual epidemiological data in Korea, we estimated the infection probability for each age group under different levels of social distancing implemented in Korea and investigated the effective age-dependent vaccination strategies to reduce the confirmed cases and fatalities of COVID-19. We found that, in a lower level of social distancing, vaccination priority for the age groups with the highest transmission rates will reduce the incidence mostly, but, in higher levels of social distancing, prioritizing vaccination for the elderly age group reduces the infection incidences more effectively. To reduce mortalities, vaccination priority for the elderly age group is the best strategy in all scenarios of levels of social distancing. Furthermore, we investigated the effect of vaccine supply and efficacy on the reduction in incidence and mortality.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:4240-:d:537511
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    References listed on IDEAS

    as
    1. Zindoga Mukandavire & Farai Nyabadza & Noble J Malunguza & Diego F Cuadros & Tinevimbo Shiri & Godfrey Musuka, 2020. "Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-11, July.
    2. Yongin Choi & James Slghee Kim & Heejin Choi & Hyojung Lee & Chang Hyeong Lee, 2020. "Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    3. Semagn Mekonnen Abate & Siraj Ahmed Ali & Bahiru Mantfardo & Bivash Basu, 2020. "Rate of Intensive Care Unit admission and outcomes among patients with coronavirus: A systematic review and Meta-analysis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-19, July.
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    Cited by:

    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Erdoğan, Güneş & Yücel, Eda & Kiavash, Parinaz & Salman, F. Sibel, 2024. "Fair and effective vaccine allocation during a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    3. N. Shamsi Gamchi & M. Esmaeili, 2023. "A novel mathematical model for prioritization of individuals to receive vaccine considering governmental health protocols," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(4), pages 633-646, June.
    4. 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.

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