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West Nile virus (WNV) was first detected in North America during 1999, and has since spread throughout the contiguous USA. West Nile virus causes West Nile fever and the more severe West Nile neuroinvasive disease. As part of a WNV vector surveillance program, we collected mosquitoes in Lubbock, Texas, using CO2-baited encephalitic vector survey (EVS) traps. During 219 wk from 2009 through 2017, EVS traps were operated for 1,748 trap nights, resulting in more than 101,000 mosquitoes captured. Weekly, selected female mosquito specimens were pooled by species and trap site, and screened for WNV using reverse transcription–polymerase chain reaction assay. Mosquitoes positive for WNV were detected during 16.9% (37/219) of the weeks. Using this information, we constructed a statistical model to predict the probability of detecting an infection within a mosquito pool as a factor of weather variables. The final model indicated that detection of WNV in mosquitoes was negatively associated with the week of year squared and average wind from 3 wk prior to sampling, and was positively associated with week of year, average visibility, average humidity from 2 wk prior to sampling, and average dew point from 4 wk prior to sampling. The model developed in this study may aid public health and vector control programs in swift and effective decision making relative to city-wide mosquito control efforts by predicting when the chances of mosquitoes having WNV are at their greatest.

Predictive Modeling for West Nile Virus and Mosquito Surveillance in Lubbock, Texas