Frequentist inference for semi-mechanistic epidemic models with interventions
成果类型：
Article
署名作者：
Bong, Heejong; Ventura, Valerie; Wasserman, Larry
署名单位：
University of Michigan System; University of Michigan; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1093/jrsssb/qkae110
发表日期：
2025
页码：
701-722
关键词：
摘要：
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.