Problem
Research questions and friction points this paper is trying to address.
infectious disease prevalence
longitudinal testing programs
symptomatic testing
contact-tracing testing
observation process
A Counterfactual Framework for Estimating Infectious Disease Prevalence under Repeated Testing with Symptomatic and Contact-Tracing Components
🤖 AI Summary
本文针对重复检测中估计传染病流行率的问题，提出了一种反事实框架方法，通过建模检测过程来无偏估计疾病流行率。
📝 Abstract
This paper addresses the problem of estimating infectious disease prevalence under longitudinal testing programs that include scheduled, symptomatic, and contact-tracing testing. Our study is motivated by data from The Ohio State University, where a mandatory once-per-week COVID-19 testing and isolation program was implemented during the Fall 2020 semester, supplemented by additional testing for symptomatic individuals and identified contacts. In this setting, the probability of being tested depends on symptoms or contact-tracing status, creating a complex observation process. We develop a counterfactual framework that links the observation process to a hypothetical process in which infection is prevented. This formulation enables unbiased estimation of disease prevalence by modeling the testing process, possibly nonparametrically, without requiring explicit modeling of transmission dynamics, even though the testing and infection processes are jointly dependent.
Innovation
Methods, ideas, or system contributions that make the work stand out.
counterfactual framework
infectious disease prevalence
longitudinal testing programs
unbiased estimation
🔎 Similar Papers
