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Since its introduction to North America in 1999, West Nile virus (WNV) has become the most widespread mosquito‐borne disease in the United States. Climatic conditions significantly influence transmission dynamics. While temperature, precipitation, and humidity are known to affect mosquito populations and virus replication, wind speed is often neglected in transmission models despite its potential to alter mosquito behavior and facilitate mosquito dispersal. This study incorporates wind speed into climate‐based WNV models to compare its effects in Louisiana and South Dakota, two U.S. states with contrasting climates, land cover, and vector and host species. From 2004 to 2022, we analyzed weekly WNV human case data in relation to daily meteorological data. The relationships were modeled using logistic regression with distributed lag effects. Incorporating wind speed consistently enhanced the fit of climate‐based models across both states, as evidenced by the Akaike Information Criterion. Higher‐than‐normal wind speeds were associated with decreased WNV cases over specific lag periods, suggesting that increased wind speed may inhibit mosquito activity and reduce virus transmission. Differences in how temperature and moisture‐related variables influenced the two regions highlight the importance of considering regional climatic contexts. These findings demonstrate that incorporating wind speed can enhance meteorological models of mosquito‐borne diseases and reinforce the importance of considering a broader range of climatic factors beyond temperature and precipitation. Understanding these regional variations is essential for predicting local climatic influences on disease transmission, which can support the implementation of more targeted and effective public health strategies. Plain Language Summary West Nile virus (WNV) has become the most common mosquito‐borne disease in the United States since it first appeared in North America in 1999. While climate variables such as temperature, precipitation, and humidity influence the spread of WNV, many studies have overlooked the role of wind speed. This research examines the impact of wind speed, temperature, humidity, and rainfall on WNV cases in Louisiana and South Dakota, two states with distinct climates. Using data from 2004 to 2022, we compared statistical models of WNV cases with and without wind speed to determine if including wind speed improved the models' performance. We found that including wind speed consistently improved the models. Higher‐than‐normal wind speeds were associated with fewer WNV cases in subsequent months, likely because strong winds limit mosquito activity and reduce their ability to bite. Additionally, the effects of temperature, humidity, and precipitation varied between Louisiana and South Dakota, highlighting how climate variations can influence WNV across different regions. These findings suggest that wind speed should be considered when studying WNV transmission. Understanding these climate variables at a regional level helps clarify how climate influences disease risk, providing valuable insights for public health planning. Key Points Integrating wind speed into climate models consistently improved model performance for WNV cases in Louisiana and South Dakota Higher than normal wind speeds decreased reported WNV cases in both Louisiana and South Dakota Climate variables exhibited different influences on WNV incidence between the two states, underscoring the importance of regional context

Objectives West Nile virus (WNV) is the most common mosquito-borne disease in the United States. Predicting the location and timing of outbreaks would allow targeting of disease prevention and mosquito control activities. Our objective was to develop software (ArboMAP) for routine WNV forecasting using public health surveillance data and meteorological observations. Materials and Methods ArboMAP was implemented using an R markdown script for data processing, modeling, and report generation. A Google Earth Engine application was developed to summarize and download weather data. Generalized additive models were used to make county-level predictions of WNV cases. Results ArboMAP minimized the number of manual steps required to make weekly forecasts, generated information that was useful for decision-makers, and has been tested and implemented in multiple public health institutions. Discussion and Conclusion Routine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP. Lay Summary West Nile virus (WNV) is the most common mosquito-borne disease in the United States. To reduce the risk of WNV, public health agencies distribute information about how to avoid mosquito bites and use insecticides to reduce the abundances of disease-transmitting mosquitoes. Information about when and where the risk of getting WNV is highest would help these agencies to target their activities and use limited resources more efficiently. To support this goal, we developed the ArboMAP software system for predicting the risk of WNV disease in humans. ArboMAP uses information about recent weather combined with data obtained from trapping mosquitoes and testing them for presence of WNV to predict how many human cases will occur in future weeks. Predictions extend throughout the current WNV season (typically May-September) and are made for each county within a state. The system is implemented as a set of free software tools that can be used by epidemiologists in state and municipal departments of health. Feedback from public health agencies in South Dakota, Louisiana, Oklahoma, and Michigan has been incorporated to enhance the usability of the system and design visualizations that summarize the forecasts.