Abstract
BackgroundWe 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.MethodsWe 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.ResultsOccupancy 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.ConclusionsOccupancy 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.
Highlights
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
Overall, we found that occupancy modeling in conjunction with resampling strategies can overcome low test sensitivity associated with rapid tests to provide accurate SARS-CoV-2 prevalence estimates comparable to those of more accurate but slower tests
For emerging infectious diseases like COVID-19, rapid testing is essential for generating the real-time disease monitoring data that is required to inform disease management actions and minimize human health impacts [2, 6]
Summary
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. Novel modeling approaches developed for sampling rare animals in wildlife sciences hold great potential to address the inherent limitations of rapid tests [12]. Occupancy models address imperfect detection by collecting repeated samples (observations) of a species to statistically model sampling error rates and use this information to improve estimates of species’ presence/absence. Due to more resource limitations in wildlife sciences compared to human health fields, wildlife researchers have developed tools for optimizing sampling designs [20] that can be adapted to generate efficacious and accurate sampling designs for estimating human disease prevalence
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