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, viral disease. In this study, I chose to analyze two dependent variables for sequential sampling schemes, each representing a surrogate of infection rate in Culex mosquito populations for West Nile virus (WNv); namely, the number and percentage of mosquito pools that tested positive for the virus in real time PCR. I obtained longitudinal data of WNv infection in mosquitoes in Illinois from local study site, county, and state spatial scales, as well as, the dates of onset of confirmed human cases of WNv infection, from a public source, occurring within these spatial units through a completely anonymized process. Sampling and epidemic threshold regression lines were successfully developed with aggregation analysis for each spatial scale and dependent variable. Sequential sampling schemes were found to be most sensitive to prediction of epidemic outbreaks at the local study site scale, and relatively less sensitive at the county and state spatial scales, likely due to the effects of local transmission dynamics that were obscured at broader geographic scales by sampling error. Furthermore, the two dependent variables showed little difference in their ability to forecast West Nile virus human cases at the study site and county scales, making either a possibility for use in a sequential sampling plan.
Read
