Modeling the Transmission Mitigation Impact of Testing for Infectious Diseases

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing community transmission. Unfortunately, direct estimation of this effectiveness is difficult in practice, elevating the value of mathematical modeling that can predict it from first principles. Here, we introduce testing effectiveness [Formula: see text] , defined as the fraction by which transmission is reduced via testing and post-diagnosis isolation at the population scale, and develop a mathematical model that estimates it from the interactions of tests, within-host pathogen dynamics, and arbitrarily complex testing behaviors. While our model generalizes across pathogens, we demonstrate its flexibility through an analysis of three respiratory pathogens, influenza A, respiratory syncytial virus (RSV), and both pre-vaccine and post-vaccine era SARS-CoV-2, quantifying [Formula: see text] across post-exposure, post-symptom, and routine testing scenarios. We show that [Formula: see text] varies considerably by strategy and pathogen, with optimal testing depending on the number of tests available and when they are used. This work quantifies tradeoffs about when and how to test, providing a flexible framework to guide the use and development of current and future diagnostic tests to control transmission of infectious diseases.