A model to assess the accuracy of detecting arboviruses in mosquito pools

Vigilant surveillance of virus prevalence in mosquitoes is essential for risk assessment and outbreak prediction. Accurate virus detection methods are essential for arbovirus surveillance. We have developed a model to estimate the probability of accurately detecting a virus-positive mosquito from pooled field collections using standard molecular techniques. We discuss several factors influencing the probability of virus detection, including the number of virions in the sample, the total sample volume, and the portion of the sample volume that is being tested. Our model determines the probability of obtaining at least 1 virion in the sample that is tested. The model also determines the optimal sample volume that is required in any test to ensure a desired probability of virus detection is achieved, and can be used to support the accuracy of current tests or to optimize existing techniques.