An outbreak of pneumonia of unknown etiology in Wuhan City, Hubei Province, China was initially reported to the World Health Organization in December 2019. Global health authorities purportedly identified a novel coronavirus (SARS-CoV-2) which continues to effect most of the world. Diagnostic testing for SARS-CoV-2 continues to advance and interpreting test results is paramount. The most commonly used diagnostic test has been the reverse transcriptase polymerase chain reaction amplification. Determining test precision not only depends on operating characteristics, sensitivity and specificity, but more importantly on prevalence of the infectious in the population tested. This paper develops a numerical optimizer (model) to explore the effect of prevalence on surveillance testing accuracy. Findings herein suggest that large scale COVID-19 surveillance testing should be curbed or eliminated. Results advocate a more flexible and narrowly targeted approach to testing strategies.
Keywords: COVID-19, Prevalence, SARS-CoV-2 testing
