A Spatially Resolved and Environmentally Informed Forecast Model of West Nile Virus in Coachella Valley, California

West Nile virus (WNV) is the most significant arbovirus in the United States in terms of both morbidity and mortality. West Nile exists in a complex transmission cycle between avian hosts and the arthropod vector, Culex spp. mosquitoes. Human spillover events occur when humans are bitten by an infected mosquito and predicting these rates of infection and therefore the risk to humans may be associated with fluctuations in environmental conditions. In this study, we evaluate the hydrological and meteorological drivers associated with mosquito biology and viral development to determine if these associations can be used to forecast seasonal mosquito infection rates with WNV in the Coachella Valley of California. We developed and tested a spatially resolved ensemble forecast model of the WNV mosquito infection rate in the Coachella Valley using 17 years of mosquito surveillance data and North American Land Data Assimilation System-2 environmental data. Our multi-model inference system indicated that the combination of a cooler and dryer winter, followed by a wetter and warmer spring, and a cooler than normal summer was most predictive of the prevalence of West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk has the potential to allow local abatement districts and public health entities to implement early season interventions such as targeted adulticiding and public health messaging before human transmission occurs. Such early and targeted interventions could better mitigate the risk of WNV to humans. West Nile virus (WNV) is the most significant arbovirus in the United States and is transmitted seasonally by mosquitoes. Humans are most at risk when they are in close proximity to infected mosquitoes. Predicting the risk to humans is not straightforward. In this study, we use deviations in climate associated with mosquito biology and viral development to forecast seasonal West Nile risk in the Coachella Valley of California. We developed a statistical model of WNV transmission in the Coachella Valley using 17 years of mosquito surveillance data and environmental data. Our model indicated that the combination of a cooler and dryer winter followed by a wetter and warmer spring and a cooler than normal summer was the combination of environmental events most associated with West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk could assist local public health entities implement early season interventions to better mitigate the risk of WNV to humans in the Coachella Valley. Environmentally informed West Nile virus (WNV) forecast modelOur forecast shows that a dry, cool winter, followed by a wet, warm spring, and a cool summer promotes WNVEarly season forecasts are a potential decision tool to inform public health and mosquito abatement intervention