Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies of combatting the pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information to assess the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors to quantify intervention impacts at a finer granularity. A data assimilation framework is developed to estimate these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is built to quantify the impacts of intervention strategies by monitoring the evolution of the estimated parameters. We reveal the intervention impacts in European countries and Wuhan and the resurgence risk in the United States.

中文翻译：

---

大流行病的贝叶斯更新方案：估计 COVID-19 的感染动态


流行病模型在理解和应对新出现的 COVID-19 大流行方面发挥着关键作用。广泛使用的区室模型是静态的，在评估抗击大流行的干预策略方面作用有限。应用数据同化技术，我们提出了一种贝叶斯更新方法，利用可观察信息估计流行病学参数，以评估不同干预策略的影响。我们采用简洁的更新模型，并通过将瞬时再生数 Rt 的减少分解为缓解和抑制因素来提出新的参数，以更细粒度地量化干预影响。开发了数据同化框架来估计这些参数，包括构建观测函数和开发贝叶斯更新方案。建立统计分析框架，通过监测估计参数的演变来量化干预策略的影响。我们揭示了欧洲国家和武汉的干预影响以及美国的复苏风险。