17 - A Connecticut Yankee in a mosquito's court: mapping mosquito and arbovirus activity in Connecticut

Using mosquito and arbovirus data from the Connecticut Agricultural Experiment Station’s (CAES) statewide mosquito and arbovirus surveillance program (2001 – 2020), we developed a risk projection pipeline that projects risk of detecting West Nile virus (WNV) in un-sampled spaces within the state. We used boosted regression tree (BRT) methodologies to first develop predictive algorithms of Culex pipiens collections in gravid traps based on average surveillance effort, climate and land cover variables only; these algorithms were then nested within a BRT algorithm of WNV detection probabilities. Results of the WNV prediction models were aggregated to the town level and then successfully validated against two separate data sets: 1) reported human case data (2001 – 2022) and 2) observed WNV detection rates in mosquitoes sampled in 2021 – 2022. Overall, our WNV detection predictions explained a significant amount of variance in human WNV case data and mosquito surveillance data. The over-arching goal of this research is to develop interactive, online risk maps which can be released to the public by CAES in real-time. The predicted utility of such risk maps is that they will allow users to estimate arbovirus risk at locations not explicitly sampled by the surveillance network.