Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, MCMC sampling of the posterior, computation of the model evidence, and the determination of parameter sensitivities via the Fisher information matrix. Our methodology is implemented in PyRoss, an open-source platform for analysis of epidemiological compartment models.
翻译:流行病学预测被流行病学基本过程和数据获取监测过程的不确定性所困扰,我们提出了一种贝叶斯推论方法,用(可能)非静止、连续时间、马尔科夫人口过程模拟的流行病,对这些不确定性进行量化;该方法的效率来自对大量人口有效的可能性的功能中心限度理论近似值;我们通过分析联合王国COVID-19大流行的早期阶段的方法展示了该方法,该方法基于死亡人数的年龄结构数据,包括事后估计、外延取样、模型证据的计算和通过渔业信息矩阵确定参数敏感性;我们的方法在PyRos实施,这是一个用于分析流行病学区划模型的公开源平台。