The Impact of Increasing Disease Prevalence, False Omissions, and Diagnostic Uncertainty on Coronavirus Disease 2019 (COVID-19) Test Performance

Context.-Coronavirus disease 2019 (COVID-19) test performance depends on predictive values in settings of increasing disease prevalence. Geospatially distributed diagnostics with minimal uncertainty facilitate efficient point-of-need strategies. Objectives.-To use original mathematics to interpret COVID-19 test metrics; assess US Food and Drug Administration Emergency Use Authorizations and Health Canada targets; compare predictive values for multiplex, antigen, polymerase chain reaction kit, point-of-care antibody, and home tests; enhance test performance; and improve decision-making. Design.-PubMed/newsprint-generated articles documenting prevalence. Mathematica and open access software helped perform recursive calculations, graph multivariate relationships, and visualize performance by comparing predictive value geometric mean-squared patterns. Results.-Tiered sensitivity/specificity comprised: T1, 90%, 95%; T2, 95%, 97.5%; and T3, 100%, >= 99%. Tier 1 false negatives exceeded true negatives at >90.5% prevalence; false positives exceeded true positives at <5.3% prevalence. High-sensitivity/specificity tests reduced false negatives and false positives, yielding superior predictive values. Recursive testing improved predictive values. Visual logistics facilitated test comparisons. Antigen test quality fell off as prevalence increased. Multiplex severe acute respiratory syndrome (SARS)-CoV-2)*influenza A/B*respiratory syncytial virus testing performed reasonably well compared with tier 3. Tier 3 performance with a tier 2 confidence band lower limit will generate excellent performance and reliability. Conclusions.-The overriding principle is to select the best combined performance and reliability pattern for the prevalence bracket. Some public health professionals recommend repetitive testing to compensate for low sensitivity. More logically, improved COVID-19 assays with less uncertainty conserve resources. Multiplex differentiation of COVID-19 from influenza A/B-respiratory syncytial virus represents an effective strategy if seasonal flu surges next year.

Automated summary

The study used original mathematics to interpret COVID-19 test metrics and compare predictive values for different types of tests. High sensitivity/specificity tests were found to improve predictive values, and multiplex testing can effectively differentiate COVID-19 from influenza/RSV.