Operational Analysis for Coronavirus Testing
Abstract
Testing will remain a key tool for those managing health care and making health policy for the current coronavirus pandemic, and testing will probably be an important tool in future pandemics. Because of test errors, the observed fraction of positive tests, the surface positivity, is generally different from the underlying incidence rate of the disease. We model, using both analytical and simulation tools, the process of testing to address (1) how to go from positivity to a point estimate incidence rate; (2) how to compute a reasonable range of possible incidence rates, given the models and data; (3) how to compare different levels of positivity in light of test errors, particularly false negatives; and (4) how to compute the risk (defined as including one infected individual) of groups of different sizes, given the estimate of incidence rate. Our approach is based on modeling the process generating test data in which the true state of the world (incidence rate, probability of a false negative test, and probability of a false positive test) is known. This allows us to compare analytical predictions with a known situation, thus providing confidence when the tools are used when the true state of the world is not known.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2021
- Accession Number
- AD1152298
Entities
People
- Alan C. Brown
- Marc Mangel