We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterizes the time varying reproduction number $R_t$ through a regression framework in which covariates can e.g. be governmental interventions or changes in mobility patterns. This allows a joint fit across regions and partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics, whose validity was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and observations deriving from them, including deaths, cases, hospitalizations, ICU admissions and seroprevalence surveys. One issue surrounding our model's use during the COVID-19 pandemic is the confounded nature of NPIs and mobility. We use our framework to explore this issue. We have open sourced an R package epidemia implementing our approach in Stan. Versions of the model are used by New York State, Tennessee and Scotland to estimate the current situation and make policy decisions.
翻译:我们建议采用通用的贝叶斯方法来模拟诸如COVID-19等流行病。这一方法产生于在该流行病期间进行的具体分析,特别是关于非制药干预在减少COVID-19传播方面对11个欧洲国家的影响的分析。模型参数通过一个退缩框架,将不同的复制时间编号(R$t$)分为不同的复制编号(R$),在这个框架内,共变可以是政府干预或流动性模式的变化。这可以使跨区域联合并部分集中起来分享力量。这一创新对于我们及时估计锁定和其他NPI在欧洲流行病中的影响至关重要,而后者的有效性由随后的流行病过程所证明。我们的框架为潜在感染和从这些疾病中得出的观察提供了完全的基因化模型,包括死亡、病例、住院、ICU的接收和血清反应调查。在COVID-19大流行病期间,我们模型使用的一个问题是NPIs和流动性的内在性质。我们使用我们的框架来探讨这一问题。我们用一个公开来源的R包来执行我们在Stanstal上采用的方法和 Stan国家采用的模式。