Estimating population infection rates from non-random testing data: Evidence from the COVID-19 pandemic

• Published in: PloS one Vol. 19; no. 9; p. e0311001
• Main Authors: Benatia, David, Godefroy, Raphael, Lewis, Joshua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.09.2024
• Subjects: Asymptomatic; Bias; Biology and Life Sciences; Canada; COVID-19; COVID-19 - diagnosis; COVID-19 - epidemiology; COVID-19 - mortality; COVID-19 diagnostic tests; COVID-19 Testing - methods; Data mining; Disease transmission; Epidemics; Estimates; Fatalities; Health care; Humans; Infections; Infectious diseases; Medicine and Health Sciences; Methods; Pandemics; People and places; Population statistics; Prevalence; Prevalence studies (Epidemiology); Public health; Real time; Risk factors; SARS-CoV-2 - isolation & purification; Seroepidemiologic Studies; Serology; Severe acute respiratory syndrome coronavirus 2; Statistics; United States - epidemiology; Viral diseases; United States; Canada; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0311001

To effectively respond to an emerging infectious disease outbreak, policymakers need timely and accurate measures of disease prevalence in the general population. This paper presents a new methodology to estimate real-time population infection rates from non-random testing data. The approach compares how the observed positivity rate varies with the size of the tested population and applies this gradient to infer total population infections. Applying this methodology to daily testing data across U.S. states during the first wave of the COVID-19 pandemic, we estimated widespread undiagnosed COVID-19 infections. Nationwide, we found that for every identified case, there were 12 population infections. Our prevalence estimates align with results from seroprevalence surveys, alternate approaches to measuring COVID-19 infections, and total excess mortality during the first wave of the pandemic.