Sequential sampling schemes for predicting West Nile virus epidemics utilizing Culex mosquito infection rates

Sequential sampling of insects offers the benefits of increased efficiency of sampling and support of decision making. However, its applications to public health entomology are rare, even though it could be a powerful tool in guiding sampling programs for vectors, and in forecasting risk of epidemics of mosquito-borne arboviral disease. Most previous studies used raw mosquito population data as input into sequential sampling schemes. In this study, we chose to use by contrast as the dependent variable a surrogate of infection rate in Culex mosquito populations for West Nile virus (WNV); namely, the percentage of mosquito pools that tested positive for the virus in real time PCR. We obtained longitudinal data of WNV infection in mosquitoes in Illinois from the local, county, and state spatial scales. We gathered geocoded addresses of confirmed human cases of WNV from the same spatial scales and years. Sampling and epidemic threshold regression lines were successfully developed from sampling data and aggregation analysis. Sequential sampling schemes were found to be most sensitive to prediction of epidemic outbreaks at local scales, probably due to the local transmission dynamics that became obscured by regional processes at broader scales.