Background We evaluated whether occupancy modeling, an approach developed for detecting rare wildlife species, could overcome inherent accuracy limitations associated with rapid disease tests to generate fast, accurate, and affordable SARS-CoV-2 prevalence estimates. Occupancy modeling uses repeated sampling to estimate probability of false negative results, like those linked to rapid tests, for generating unbiased prevalence estimates. Methods We developed a simulation study to estimate SARS-CoV-2 prevalence using rapid, low-sensitivity, low-cost tests and slower, high-sensitivity, higher cost tests across a range of disease prevalence and sampling strategies. Results Occupancy modeling overcame the low sensitivity of rapid tests to generate prevalence estimates comparable to more accurate, slower tests. Moreover, minimal repeated sampling was required to offset low test sensitivity at low disease prevalence (0.1%), when rapid testing is most critical for informing disease management. Conclusions Occupancy modeling enables the use of rapid tests to provide accurate, affordable, real-time estimates of the prevalence of emerging infectious diseases like SARS-CoV-2. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-10609-y.
【저자키워드】 Occupancy modeling, Optimal sampling, Repeated sampling, Sampling strategies, 【초록키워드】 SARS-CoV-2, Infectious diseases, Emerging infectious diseases, Infectious disease, Prevalence, Probability, sensitivity, Sampling strategies, Disease prevalence, Rapid test, Accuracy, management, estimate, estimates, disease, Critical, Rapid tests, False negative, Occupancy, supplementary material, limitation, false negative results, repeated, approach, Result, evaluated, required, generate, overcome, comparable, inherent, offset, 【제목키워드】 SARS-CoV-2, Prevalence, sensitivity, estimate, Occupancy, overcome,