Casey Middleton, Interdisciplinary Quantitative Biology Program, University of Colorado Boulder

A Probabilistic Model of Disease Mitigation via Community Testing

The COVID-19 pandemic has shown us the value of regular community testing to break transmission chains and control community outbreaks. However, not all tests are created equal. High sensitivity tests are expensive and often require several days before results are delivered. Low sensitivity tests may be administered more frequently and provide immediate results, but cannot detect the earliest stages of viral growth. Given these limitations, how do we optimize control? I will present a probabilistic model that connects within-host viral kinetics with viral detection testing attributes to predict the effectiveness of a given testing regimen. This flexible model framework allows us to explore testing strategies for multiple respiratory viruses and testing scenarios.