What is it about?

It is now known that many of the cases of Covid-19 were asymptomatic but still infectious. Because of this, when a population is tested for Covid-19, it can be divided into four groups: infected and symptomatic, infected and asymptomatic, not infected and symptomatic and not infected and not symptomatic. There are also test errors, in which infected individuals test negative (a false negative) and uninfected individuals test positive (a false positive). The challenge is to use the test results to determine the fraction of individuals who are infected and symptomatic and the fraction of individuals who are infected and asymptomatic.

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Why is it important?

During the Covid-19 pandemic, and likely in future ones, asymptomatic cases spread the disease. The methods in this paper allow the computation of the risk of including asymptomatic individuals in groups of different sizes, given the test results. That is, risk is not binary (risky or not) but graded (risk increases with the number of individuals in a group).


This work arose in collaboration with a colleague at the Johns Hopkins Applied Physics Laboratory, who was seconded to Howard County Maryland (where APL is located) during the pandemic to help with the analysis of test data and planning management of the disease. We wrote two previous reports that can be found on the APL website: https://www.jhuapl.edu/work/publications

Marc Mangel
University of California Santa Cruz

Read the Original

This page is a summary of: Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections, PLoS ONE, February 2023, PLOS,
DOI: 10.1371/journal.pone.0281710.
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