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 paper 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.

翻译：公共卫生干预措施对流行病的影响通常通过将干预措施添加到流行病模型中进行估计。在新冠疫情（Covid-19）期间，大量论文使用此类方法进行情景预测。其中大多数论文采用贝叶斯方法估计模型参数。本文展示了如何运用频率学派方法估计这些效应，从而避免指定先验分布。同时，针对存在多个不同地理区域的情况，我们采用无模型收缩方法改进估计效果。这使我们能够在无需指定层次模型的前提下，从不同区域借用统计强度，同时获得具有正确覆盖率的置信区间。全文聚焦于一种半机制模型，该模型为仓室模型方法提供了简单易处理的替代方案。