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Youngsuk Ko 2 Articles
Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study
Youngsuk Ko, Jacob Lee, Yubin Seo, Eunok Jung
Epidemiol Health. 2021;43:e2021059.   Published online September 8, 2021
DOI: https://doi.org/10.4178/epih.e2021059
  • 4,709 View
  • 118 Download
  • 3 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
This study aims to analyze the possibility and conditions of maintaining an effective reproductive number below 1 using a mathematical model.
METHODS
The total population was divided into five age groups (0-17, 18-29, 30-59, 60-74, and ≥75 years). Maximum likelihood estimation (MLE) was used to estimate the transmission rate of each age group. Mathematical model simulation was conducted until December 31, 2021, by establishing various strategies for vaccination and social distancing without considering variants.
RESULTS
MLE results revealed that the group aged 0-17 years had a lower risk of transmission than other age groups, and the older age group had relatively high risks of infection. If 70% of the population will be vaccinated by the end of 2021, then simulations showed that even if social distancing was eased, the effective reproductive number would remain below 1 near August if it was not at the level of the third re-spreading period. However, if social distancing was eased and it reached the level of the re-spreading period, the effective reproductive number could be below 1 at the end of 2021.
CONCLUSIONS
Considering both stable and worsened situations, simulation results emphasized that sufficient vaccine supply and control of the epidemic by maintaining social distancing to prevent an outbreak at the level of the re-spreading period are necessary to minimize mortality and maintain the effective reproductive number below 1.
Summary
Korean summary
본 연구에서는 질병관리청에서 제공하는 개별 확진자 데이터에 확률통계적 방법을 적용하여 연령군 간의 감염전파 행렬을 추정하였으며 연령군을 고려한 수리모델에 적용되었다. 본 연구에서 우리는 2020년 10월부터 2021년 5월까지 한국에서의 코로나19 유행상황을 정책 구간에 따라 분석하였으며 이를 토대로 거리두기 완화 수준에 따라 거리두기 완화 상태에서도 지속적으로 유효감염재생산지수가 1보다 작아지는 시점이 달라질 수 있음을 보인다.
Key Message
In this research, we estimated age-group-specified transmission rate matrix by applying maximum likelihood estimation into individual based data which was provided by Korea Disease Control and Prevention Agency. Our model simulation showed the moment, when the effective reproductive number is consistently below 1 even the distancing is eased, is ranged from August to the end of 2021 depending on the intensity of the social distancing during eased phase.

Citations

Citations to this article as recorded by  
  • 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
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Quantifying the Effects of Non-Pharmaceutical and Pharmaceutical Interventions Against Covid-19 Epidemic in the Republic of Korea: Mathematical Model-Based Approach Considering Age Groups and the Delta Variant
    Youngsuk Ko, Victoria May P. Mendoza, Yubin Seo, Jacob Lee, Yeonju Kim, Donghyok Kwon, Eunok Jung, E. Augeraud, M. Banerjee, J.-S. Dhersin, A. d'Onofrio, T. Lipniacki, S. Petrovskii, Chi Tran, A. Veber-Delattre, E. Vergu, V. Volpert
    Mathematical Modelling of Natural Phenomena.2022; 17: 39.     CrossRef
  • Dietary Behavior and Diet Quality in the Korean Adult Population by Income Level before and after the COVID-19 Pandemic: Using the Korean National Health and Nutrition Examination Survey (2019-2020)
    Hye-Min Na, Bok-Mi Jung
    The Korean Journal of Community Living Science.2022; 33(3): 397.     CrossRef
Ebola virus disease outbreak in Korea: use of a mathematical model and stochastic simulation to estimate risk
Youngsuk Ko, Seok-Min Lee, Soyoung Kim, Moran Ki, Eunok Jung
Epidemiol Health. 2019;41:e2019048.   Published online November 24, 2019
DOI: https://doi.org/10.4178/epih.e2019048
  • 7,529 View
  • 192 Download
  • 1 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
According to the World Health Organization, there have been frequent reports of Ebola virus disease (EVD) since the 2014 EVD pandemic in West Africa. We aim to estimate the outbreak scale when an EVD infected person arrives in Korea.
METHODS
Western Africa EVD epidemic mathematical model SEIJR or SEIJQR was modified to create a Korean EVD outbreak model. The expected number of EVD patients and outbreak duration were calculated by stochastic simulation under the scenarios of Best case, Diagnosis delay, and Case missing.
RESULTS
The 2,000 trials of stochastic simulation for each scenario demonstrated the following results: The possible median number of patients is 2 and the estimated maximum number is 11 when the government intervention is proceeded immediately right after the first EVD case is confirmed. With a 6-day delay in diagnosis of the first case, the median number of patients becomes 7, and the maximum, 20. If the first case is missed and the government intervention is not activated until 2 cases of secondary infection occur, the median number of patients is estimated at 15, and the maximum, at 35.
CONCLUSIONS
Timely and rigorous diagnosis is important to reduce the spreading scale of infection when a new communicable disease is inflowed into Korea. Moreover, it is imperative to strengthen the local surveillance system and diagnostic protocols to avoid missing cases of secondary infection.
Summary
Korean summary
본 연구는 수학적 모델과 확률 시뮬레이션 기법을 이용하여 국내에 유입되지 않았던 에볼라바이러스병(EVD)의 확산 위험도를 정량적으로 예측하는 첫 번째 연구이다. 또한 이 연구를 통해 에볼라바이러스병 환자의 유입 시 발생 가능한 진단 지연 혹은 유입 미인지 상황을 가정하여 발생할 수 있는 2차 감염자 수 및 감염 종식까지의 기간을 계산했고 에볼라바이러스 유입 대비 실시간모니터링의 중요성과 확산 시 상황에 따른 최대 일일 환자수를 합리적으로 제시할 수 있다.
Key Message

Citations

Citations to this article as recorded by  
  • Estimating the Transmission Risk of COVID-19 in Nigeria: A Mathematical Modelling Approach
    Irany FA, Akwafuo SE, Abah T, Mikler AR
    Journal of Health Care and Research.2020; 1(3): 135.     CrossRef

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