Quantitative Comparison of SARS-CoV-2 Nucleic Acid Amplification Test and Antigen Testing Algorithms: A Decision Analysis Simulation Model

BackgroundAntigen tests for SARS-CoV-2 offer advantages over nucleic acid amplification tests (NAATs, such as RT-PCR), including lower cost and rapid return of results, but show reduced sensitivity. Public health organizations continue to recommend different strategies for utilizing NAATs and antigen tests in various settings. There has not yet been a quantitative comparison of the expected performance of these strategies. MethodsWe utilized a decision analysis approach to simulate the expected outcomes of six algorithms for implementing NAAT and antigen testing, analogous to testing strategies recommended by public health organizations. Each algorithm was simulated 50,000 times for four SARS-CoV-2 infection prevalence levels ranging from 5% to 20% in a population of 100000 persons seeking testing. Primary outcomes were number of missed cases, number of false-positive diagnoses, and total test volumes. Outcome medians and 95% uncertainty ranges (URs) were reported. ResultsAlgorithms that use NAATs to confirm all negative antigen results minimized missed cases but required high NAAT capacity: 92,200 (95% UR: 91,200-93,200) tests (in addition to 100,000 antigen tests) at 10% prevalence. Substituting repeat antigen testing in lieu of NAAT confirmation of all initial negative antigen tests resulted in 2,280 missed cases (95% UR: 1,507-3,067) at 10% prevalence. Selective use of NAATs to confirm antigen results when discordant with symptom status (e.g., symptomatic persons with negative antigen results) resulted in the most efficient use of NAATs, with 25 NAATs (95% UR: 13-57) needed to detect one additional case at 10% prevalence compared to exclusive use of antigen tests. ConclusionsNo single SARS-CoV-2 testing algorithm is likely to be optimal across settings with different levels of prevalence and for all programmatic priorities; each presents a trade-off between prioritized outcomes and resource constraints. This analysis provides a framework for selecting setting-specific strategies to achieve acceptable balances and trade-offs between programmatic priorities and constraints. DisclaimerThe findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.