论文标题
有效的贝叶斯推断完全随机流行病学模型，应用于Covid-19
Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19
论文作者
论文摘要
流行病学预测被关于基本流行病学过程以及获取数据的监视过程的不确定性所困扰。我们提出了一种贝叶斯推论方法，该方法可以量化这些不确定性，以实现（可能是非平稳，连续时间，马尔可夫人口过程）建模的流行病。该方法的效率来自功能中心极限定理的可能性，对大种群有效。我们通过分析英国Covid-19大流行的早期阶段，基于死亡人数的年龄结构数据来证明这种方法。这包括最大的后验估计值，后验的MCMC采样，模型证据的计算以及通过Fisher信息矩阵确定参数敏感性。我们的方法是在Pyross（用于分析流行病学隔室模型的开源平台）中实现的。
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.
