Bayesian evidence synthesis for modelling SARS-CoV-2 transmission
采用贝叶斯框架综合公开数据，通过离散时间随机流行病模型估计总感染数，并利用移动信息预测感染率，评估了变分贝叶斯和哈密顿蒙特卡洛方法，为决策提供直观的传播动态信息。
Abstract The acute phase of the COVID-19 pandemic has made apparent the need for decision support based upon accurate epidemic modelling. This process is substantially hampered by under-reporting of cases and related data incompleteness issues. In this article, we adopt the Bayesian paradigm and synthesize publicly available data via a discrete-time stochastic epidemic modelling framework. The models allow for estimating the total number of infections while accounting for the endemic phase of the pandemic. We assess the prediction of the infection rate utilizing mobility information, notably the principal components of the mobility data. We evaluate variational Bayes in this context and find that Hamiltonian Monte Carlo offers a robust inference alternative for such models. We elaborate upon vector analysis of the epidemic dynamics, thus enriching the traditional tools used for decision making. In particular, we show how certain two-dimensional plots on the phase plane may yield intuitive information regarding the speed and the type of transmission dynamics. We investigate the potential of a two-stage analysis as a consequence of cutting feedback, for inference on certain functionals of the model parameters. Finally, we show that a point mass on critical parameters is overly restrictive and investigate informative priors as a suitable alternative.