Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea.
Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
Nat Commun. 2024 Oct 9;15(1):8734. doi: 10.1038/s41467-024-53095-7.
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.
流行病学参数，如繁殖数、潜伏期和传染期，为传染病的传播提供了关键信息，并直接为干预策略提供了依据。这些参数通常是通过数学模型来估计的，为了简单起见，这些模型假设历史独立动力学是不现实的。这意味着在潜伏期内感染的机会或在传染期内恢复的机会保持不变，而实际上，这些机会随着时间的推移而变化。在这里，我们发现，由于这种假设，传统方法在流行病学参数估计中会产生严重的偏差。为了解决这个偏差，我们通过采用更现实的历史相关疾病动力学，开发了一种贝叶斯推断方法。与仅从通过测试容易获得的确诊病例数据中得出的传统方法相比，我们的方法更准确和精确地估计了繁殖数。它还揭示了在韩国 2020 年 COVID-19 大流行期间，传染期分布是如何随时间变化的。我们还提供了一个用户友好的包，IONISE，它可以自动执行这种方法。
