한빛사논문
- 조회199
고려대학교
Hyukpyo Hong 1,2,8,9, Eunjin Eom 3,9, Hyojung Lee 4, Sunhwa Choi 5,*, Boseung Choi 2,6,7,* & Jae Kyoung Kim 1,2,*
1Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea.
2Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea.
3Department of Economic Statistics, Korea University, Sejong 30019, Republic of Korea.
4Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea.
5Innovation Center for Industrial Mathematics, National Institute for Mathematical Sciences, Seongnam 13449, Republic of Korea.
6Division of Big Data Science, Korea University, Sejong 30019, Republic of Korea.
7College of Public Health, The Ohio State University, OH 43210, USA.
8Present address: Department of Mathematics, University of Wisconsin–Madison, Madison, WI 53706, USA.
9These authors contributed equally: Hyukpyo Hong, Eunjin Eom.
*Corresponding authors: correspondence to Sunhwa Choi, Boseung Choi or Jae Kyoung Kim
Abstract
Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.
논문정보
- Research article
- 2024년 10월 (BRIC 등록일 2024-10-10)
- 국내(교신)+국외 연구진
- 생명과학 > 감염생물학
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