Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region, China.
Data Science Institute, Imperial College London, London, United Kingdom.
PLoS Comput Biol. 2022 Feb 23;18(2):e1009807. doi: 10.1371/journal.pcbi.1009807. eCollection 2022 Feb.
Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
估计流行病学参数的变化，如瞬时繁殖数 Rt，对于理解传染病的传播动态非常重要。当前对时变流行病学参数的估计常常面临滞后观测、平均推断和不确定性的不当量化等问题。为了解决这些问题，我们提出了一种贝叶斯数据同化框架来进行时变参数估计。具体来说，该框架应用于估计新兴传染病期间的瞬时繁殖数 Rt，从而产生了最先进的“DARt”系统。使用 DARt，通过将观测延迟纳入感染和 Rt 的联合推断来解决观测滞后引起的时间不匹配问题；通过即时更新新观测值并开发一种模型选择机制来捕捉突然变化，克服了平均推断的缺点；通过贝叶斯平滑来量化和减少不确定性。我们验证了 DARt 的性能，并展示了其在描述 COVID-19 传播动态方面的强大功能。所提出的方法为基于报告数据进行准确和及时的传播动力学估计提供了一种有前途的解决方案。
