Estimating population infection rates from non-random testing data: Evidence from the COVID-19 pandemic
David Benatia, Raphael Godefroy, Joshua Lewis
TL;DR
This paper introduces a new method to estimate how many people are infected in a population using non-random testing data, showing that many more people had COVID-19 than were officially diagnosed.
Contribution
The paper introduces a novel methodology to estimate real-time infection rates using non-random testing data.
Findings
For every identified case of COVID-19, there were 12 estimated infections in the population.
The estimates align with seroprevalence surveys and excess mortality data during the pandemic's first wave.
Abstract
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.
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Taxonomy
TopicsCOVID-19 epidemiological studies · SARS-CoV-2 and COVID-19 Research · Data-Driven Disease Surveillance