Operational Analysis for Coronavirus Testing: Recommendations for Practice
Abstract
Even though vaccines for coronavirus are increasingly available, it will be many months before sufficient herd immunity is achieved. Thus, testing remains a key tool for those managing health care and making policy decisions. Test errors, both false positive tests and false negative tests, mean that the surface positivity (the observed fraction of tests that are positive) does not accurately represent the incidence rate (the unobserved fraction of individuals infected with coronavirus). In this report, directed to individuals tasked with providing analytical advice to policymakers, we describe a method for translating from the surface positivity to a point estimate for the incidence rate, then to an appropriate range of values for the incidence rate, and finally to the risk (defined as the probability of including one infected individual) associated with groups of different sizes. The method is summarized in four equations that can be implemented in a spreadsheet or using a handheld calculator. We discuss limitations of the method and provide an appendix describing the underlying mathematical models.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 04, 2021
- Accession Number
- AD1131625
Entities
People
- Alan Brown
- Marc Mangel
Organizations
- Johns Hopkins University Applied Physics Laboratory