Epidemiological forecasts are beset by uncertainties in the generative model for the disease, 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:流行病学预测被该疾病的基因模型和获取数据的监测过程的不确定性所困扰,我们提出一种贝叶斯推论方法,用(可能)非静止、连续、连续的、Markov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,根据死亡人数的年龄结构数据,展示了该方法,其中包括事后最大估计、后部生物的MCMC抽样、模型证据的计算以及通过渔业信息矩阵确定参数的敏感性;我们的方法是在PyRos这个用于分析流行病学区模型的开放源平台上实施的。