Department of Economics, Athens University of Economics and Business, Athens, Greece.
Respiratory Diseases Department, Public Health England, London, United Kingdom.
Epidemics. 2019 Dec;29:100367. doi: 10.1016/j.epidem.2019.100367. Epub 2019 Oct 5.
This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and variational inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.
本文关注的是最近统计学进展在传染病动力学推断中的应用。我们描述了使用 Hamiltonian Monte Carlo 和变分推断（在免费的 Stan 软件中实现）对一类传染病模型进行拟合的方法。我们将这两种方法应用于暴发和常规收集的观测数据。结果表明，这两种推断方法在这种情况下都是可行的，并且在统计效率与计算速度之间存在权衡。后者对于实时应用似乎尤为重要。
