Exploring the role of temperature and other environmental factors in West Nile virus incidence and prediction in California counties from 2017-2022 using a zero-inflated model.

• Published in: PLOS Neglected Tropical Diseases
• Main Author: Noah Parker
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2024
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Climate change
• Online Access: https://doi.org/10.1371/journal.pntd.0012051; https://doaj.org/article/d3a8467d0d6842258ad41e7a779c4847

West Nile virus (WNV) is the most common mosquito-borne disease in the United States, resulting in hundreds of reported cases yearly in California alone. The transmission cycle occurs mostly in birds and mosquitoes, making meteorological conditions, such as temperature, especially important to transmission characteristics. Given that future increases in temperature are all but inevitable due to worldwide climate change, determining associations between temperature and WNV incidence in humans, as well as making predictions on future cases, are important to public health agencies in California. Using surveillance data from the California Department of Public Health (CDPH), meteorological data from the National Oceanic and Atmospheric Administration (NOAA), and vector and host data from VectorSurv, we created GEE autoregressive and zero-inflated regression models to determine the role of temperature and other environmental factors in WNV incidence and predictions. An increase in temperature was found to be associated with an increase in incidence in 11 high-burden Californian counties between 2017-2022 (IRR = 1.06), holding location, time of year, and rainfall constant. A hypothetical increase of two degrees Fahrenheit-predicted for California by 2040-would have resulted in upwards of 20 excess cases per year during our study period. Using 2017-2021 as a training set, meteorological and host/vector data were able to closely predict 2022 incidence, though the models did overestimate the peak number of cases. The zero-inflated model closely predicted the low number of cases in winter months but performed worse than the GEE model during high-transmission periods. These findings suggests that climate change will, and may be already, altering transmission dynamics and incidence of WNV in California, and provides tools to help predict incidence into the future.