Preferential observation of large infectious disease outbreaks leads to consistent overestimation of intervention efficacy

Abstract

Data from infectious disease outbreaks in congregate settings are often used to elicit clues about which types of interventions may be useful in other facilities. This is commonly done using before-and-after comparisons in which the infectiousness of pre-intervention cases is compared to that of post-intervention cases and the difference is attributed to intervention impact. In this manuscript, we show how a tendency to preferentially observe large outbreaks can lead to consistent overconfidence in how effective these interventions actually are. We show, in particular, that these inferences are highly susceptible to bias when the pathogen under consideration exhibits moderate-to-high amounts of heterogeneity in infectiousness. This includes important pathogens such as SARS-CoV-2, influenza, Noroviruses, HIV, Tuberculosis, and many others

Publication
MedRxiv
Jon Zelner
Jon Zelner
Assistant Professor

My research interests include distributed robotics, mobile computing and programmable matter.

Nina Masters
Nina Masters
PhD Candidate

Nina is a doctoral candidate in Epidemiology at the University of Michigan. Her research focuses on spatial transmission models infectious diseases, the impact of clustered non-vaccination on outbreak risk, and the evolution of vaccine hesitancy.

Kelly Broen
Kelly Broen
Doctoral Student