Climate Variability and Vector-Borne Disease Dynamics: A Time-Series Analysis of Dengue, Malaria, and West Nile Virus in the United States

Background Vector-borne diseases such as Dengue, Malaria, and West Nile Virus (WNV) pose a significant public health threat in the United States. Climate change, particularly rising temperatures and altered precipitation patterns, has been implicated in the changing epidemiology of these diseases. However, the precise nature of these associations remains unclear. This study investigates the relationship between climate variability and the incidence of these diseases using a long-term time-series analysis. Methods We conducted a retrospective ecological time-series analysis using publicly available disease incidence data from Project Tycho and climate data from the PRISM database. Monthly incidence rates (per 100,000 population) for Dengue, Malaria, and WNV were analyzed alongside temperature and precipitation variables. We applied Spearman’s correlation to assess monotonic relationships, Generalized Additive Models (GAMs) to capture nonlinear climate-disease interactions, and Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) to account for lagged and seasonal effects. Results Our findings revealed that precipitation negatively correlated with all three diseases, while temperature effects varied. WNV incidence increased under drier conditions, aligning with previous research on mosquito vector-host interactions. Malaria exhibited significant non-linear associations with both temperature and precipitation, indicating threshold-dependent effects. ARIMAX modeling confirmed that climate variables significantly influenced Malaria and WNV incidence but not Dengue, suggesting that other factors, such as urbanization and vector control measures, play a dominant role in Dengue transmission. Differences between models highlighted the complexity of climate-disease interactions, with GAMs capturing nonlinear thresholds and ARIMAX models identifying lagged dependencies. Conclusion This study demonstrates that climate variability influences the transmission dynamics of vector-borne diseases in the U.S., with WNV and Malaria showing greater climate sensitivity than Dengue. The discrepancies between statistical models underscore the importance of using multiple approaches to account for nonlinear and time-lagged effects in disease forecasting. These findings emphasize the need for climate-adaptive surveillance and vector control strategies to mitigate disease transmission in a warming world.