Frequentist inference for semi-mechanistic epidemic models with interventions
本文展示了如何用频率学派方法估计公共卫生干预对流行病的影响，避免指定先验分布，并利用无模型收缩方法改进多区域估计，为流行病建模提供简单可行的替代方案。
Abstract The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use model-free shrinkage methods to improve estimation when there are many different geographic regions. This allows us to borrow strength from different regions while still getting confidence intervals with correct coverage and without having to specify a hierarchical model. Throughout, we focus on a semi-mechanistic model which provides a simple, tractable alternative to compartmental methods.