Tracking West Nile virus dynamics using viral loads from trapped mosquitoes
Mosquito-borne virus transmission is increasing globally. Due to a lack of effective vaccines or targeted treatments, mitigation relies on reducing human exposure to infectious mosquitoes. Entomological surveillance provides estimates of human risk by quantifying the prevalence of arboviruses in mosquito populations. Testing of pooled mosquitoes via RT-qPCR is often used to infer infection prevalence, however these data are often binarised and ignore semi-quantitative cycle threshold (Ct values) that may provide valuable information on virus kinetics, mosquito infectiousness, and ultimately human risk. West Nile virus (WNV) is a globally distributed arbovirus and endemic to the United States. In this study, we analysed pooled Ct values from retrospective mosquito surveillance and found substantial variation that could not be explained by laboratory factors alone. To understand how underlying viral load and epidemiological dynamics might explain this variation, we developed a multi-scale model linking pooled Ct values to heterogeneous within-host viral kinetics in mosquitoes and birds and seasonal transmission dynamics. Our modelling suggests that Ct values from positive pools might reflect a mixture of potentially infectious and non-infectious mosquitoes, indicating that a substantial proportion of PCR-positive mosquitoes might not contribute to transmission. We then developed a method to estimate mosquito infection prevalence using only pooled Ct values that distinguishes infectious from non-infectious prevalence. Estimates of overall prevalence were comparable to standard methods using binarised data and remained accurate at higher prevalences where standard approaches fail. Overall, these findings demonstrate that entomological viral load data reflect biologically meaningful variation in mosquito infection status that is lost through binarisation. Accounting for heterogeneous mosquito viral kinetics can improve interpretation of surveillance data and estimates of transmission risk, with implications for arbovirus surveillance beyond WNV.