COS 213-3 - An ECOSTRESS environmentally informed statistical model for West Nile virus infection rates among mosquitoes in the Coachella Valley, CA

Background/Question/Methods

West Nile virus (WNV) is the leading cause of domestically acquired arthropod-borne viral disease in the United States; however, interannual variation in the number of human cases is enormous. As a consequence, effective allocation of public health resources is challenging and often reactive, a circumstance that highlights the need for accurate forecasts of the burden of disease.

Results/Conclusions

Recently, we showed that accurate and reliable predictions of seasonal West Nile virus (WNV) outbreaks can be made using a hierarchical ensemble mode relating early season meteorological and hydrological factors to the annual infection rate of WNV in Coachella Valley, CA. Here, we then expanded the core model to predict trap level data by including fine scale environmental observations from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) (70 m², 1-5 days) which captures surface temperature and evapotranspiration. Using this multimodel inference system, model predictions for the Coachella Valley suggest dry winters followed by a wetting period along with a warm spring have the most significant effect on WNV transmission. We demonstrate this model-inference system can be used to better understand the spatial variability of WNV and may be an important tool to estimate the relationship between zoonotic amplification and human outbreaks. This work represents the continued evolution of an ultra-fine scale, statistically rigorous, and climate driven real-time forecast model of WNV transmission.