Frequentist Inference for Semi-mechanistic Epidemic Models with Interventions

TL;DR

This paper introduces a frequentist approach to estimate the effects of public health interventions in epidemic models, offering an alternative to Bayesian methods and improving estimation across multiple regions.

Contribution

It demonstrates how to apply frequentist methods and model-free shrinkage to semi-mechanistic epidemic models, avoiding prior specification and hierarchical modeling.

Findings

Frequentist methods provide valid confidence intervals for intervention effects.

Shrinkage improves estimation accuracy across multiple regions.

The approach offers a simple alternative to complex compartmental models.

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 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.