{"paper_id":"2b3fa2d3-fe58-4741-8e49-b3c8bf135897","body_text":"Climate Variability and Vector-Borne Disease Dynamics: A Time-Series Analysis of Dengue, Malaria, and West Nile Virus in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Variability and Vector-Borne Disease Dynamics: A Time-Series Analysis of Dengue, Malaria, and West Nile Virus in the United States Ali Hemade, Maria Akiki, Pascale Salameh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6208975/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Climate change Vector-borne diseases Dengue Malaria West Nile Virus Time-series analysis Generalized Additive Models ARIMAX Climate variability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Climate change refers to the prolonged alteration of Earth's typical weather patterns at local, regional, and global levels[ 1 ]. Since 1900, the global average temperature has risen by 1.1°C, with most of this warming occurring over the past 50 years[ 2 ]. Rising temperatures and other consequences of climate change such as shifting precipitation patterns have a profound impact on vector-borne diseases by altering the survival, distribution, and behavior of disease-causing organisms, the carriers that transmit them, and the susceptible populations they infect[ 3 , 4 ]. The dynamic relationship between temperature, vectors, and pathogens can influence the likelihood of disease transmission between humans and the potential spillover of infections from animal reservoirs to human populations[ 5 ]. As a result, climate change has already created favorable conditions for the transmission of various infectious diseases, including Lyme disease, West Nile Virus (WNV), dengue, and malaria[ 6 ]. Fleas and ticks thrive in warmer seasons, while malaria remains endemic year-round, with rising tick populations likely linked to increasingly mild winters[ 7 ]. As climate change accelerates, understanding these relationships is crucial for forecasting outbreaks and developing effective public health strategies[ 8 ]. In the United States (U.S.), dengue, malaria, and WNV represent key mosquito-borne diseases with distinct epidemiological patterns[ 9 ]. Dengue, historically endemic to tropical regions, has been reported with increasing frequency in southern U.S. states, coinciding with warming temperatures and changing precipitation patterns[ 9 ]. Malaria, once widespread in the U.S., has been largely eliminated as a locally transmitted disease but remains a concern due to imported cases and changing environmental conditions that could favor vector resurgence[ 10 ]. WNV, endemic to North America since its introduction in 1999, exhibits periodic outbreaks, particularly in warmer and drier conditions that facilitate mosquito breeding and viral transmission[ 11 ]. Despite growing evidence linking climate variability to vector-borne disease transmission, few studies have conducted long-term time-series analyses to assess these relationships at a national scale. This study employs a retrospective ecological time-series approach to examine the association between temperature, precipitation, and the incidence of dengue, malaria, and WNV in the United States. By analyzing disease patterns over several decades, this study seeks to uncover the impact of climate variability on transmission dynamics and provide insights that can enhance early warning systems, vector control interventions, and public health response strategies. Methods This study employed a retrospective ecological time-series analysis to investigate the association between climate variables and the incidence of Dengue, Malaria, and West Nile Virus (WNV) in the United States. The dataset included state-level, monthly disease incidence rates expressed as new cases per 100,000 population and corresponding climatic data, specifically precipitation and temperature. Data were sourced from publicly available epidemiological and meteorological databases spanning several decades, allowing for an extensive temporal assessment of disease trends and their potential climatic drivers. Study Population and Data Collection Disease incidence data were obtained from Project Tycho [ 12 – 14 ] a comprehensive epidemiological database that compiles historical records of infectious diseases in the United States. The dataset contained monthly confirmed case reports of Dengue, Malaria, and WNV across multiple states. To ensure consistency in reporting and comparability across different states and time periods, incidence values, standardized to reflect cases per 100,000 population, were used. Climatic data were retrieved from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) database [ 15 ], which provides high-resolution meteorological data across the United States. Monthly temperature and precipitation values were extracted and linked to disease incidence records based on state and month of reporting. The final dataset was structured to include variables for disease incidence, climate metrics, and temporal identifiers (year and month). Statistical Analysis To describe trends in disease incidence and climate variability, summary statistics were computed for each disease, including measures of central tendency and dispersion. The geographic distribution of disease burden was assessed by ranking the top ten states with the highest cumulative incidence. Data visualization techniques, including histograms and boxplots, were employed to examine the distribution of incidence rates across different states and over time. Time series decomposition was performed using Seasonal-Trend decomposition via Loess (STL) to separate observed incidence into seasonal, trend, and remainder components. The seasonal component captured recurring annual patterns in disease incidence, while the trend component represented long-term changes. The remainder component accounted for irregular fluctuations unexplained by seasonal and trend components. To assess the association between climate variables and disease incidence, Spearman’s rank correlation was used. This non-parametric method was selected due to its robustness against non-linear relationships and non-normally distributed data. Correlation coefficients, along with 95% confidence intervals and p-values, were computed to determine the strength and statistical significance of associations between precipitation, temperature, and disease incidence. Additionally, cross-correlation function (CCF) analysis was conducted to explore potential lead-lag relationships, identifying whether changes in climatic conditions preceded changes in disease incidence or vice versa. To model the relationship between climate variables and disease incidence while accounting for non-linear effects, Generalized Additive Models (GAMs) were fitted separately for each disease. These models included smoothed functions for temperature and precipitation, allowing for flexible modeling of non-linear associations. The statistical significance of these terms was evaluated using F-tests, and model fit was assessed through adjusted R-squared values and generalized cross-validation scores. For time series forecasting, an Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model was developed for each disease. The ARIMAX approach incorporated autoregressive, differencing, and moving average components while integrating climate variables as external regressors. The optimal model parameters were selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Forecasting was performed for a ten-year period, with prediction intervals generated to quantify uncertainty. Ethical Considerations This study utilized publicly available, de-identified epidemiological and climate datasets, ensuring that no individual-level data were accessed or analyzed. Given the ecological design and reliance on aggregated historical data, the study did not require ethical approval as per institutional and regulatory guidelines. Data handling and analysis were conducted in compliance with best practices for research integrity and reproducibility. Results 1. Data Summary and Descriptive Statistics The dataset consists of multiple years (1925 to 2017) of monthly disease incidence records for Dengue, Malaria, and West Nile Virus, covering all U.S. states. Each record includes disease incidence counts and incidence per 100,000 population alongside climate variables, specifically temperature and precipitation. Descriptive statistics for disease incidence across the entire population are presented in Table 1 . West Nile Virus exhibited the highest mean incidence, averaging 19.32 cases per month with substantial variability (SD = 69.59). Malaria had the highest overall case count in the dataset, yet its median monthly incidence was relatively low (0.085 cases). Dengue showed the lowest mean incidence but had months where cases peaked at 151, reflecting its tendency for sporadic outbreaks in limited regions. The median incidence for all diseases was lower than the mean, confirming that the data distribution is highly skewed, with occasional extreme outbreaks rather than sustained transmission across months. Table 1 Descriptive statistics of disease incidence (1925 to 2017) in the total population. Disease Count Mean Incidence SD Min Max Median Dengue 255 1.62 9.62 0 151.2 0.004 Malaria 8,120 2.59 8.62 0 240.6 0.085 West Nile Virus 764 19.32 69.59 0.0026 923.48 2.42 The overall distribution of disease incidence is visualized in Fig. 1 . West Nile Virus showed the widest spread of values. In contrast, Dengue and Malaria had relatively lower median values, but right-skewed distributions. The application of a logarithmic scale in the visualization helped clarify the incidence differences across diseases, particularly highlighting the presence of extreme outliers in West Nile Virus cases. 2. Geographic Distribution of Disease Incidence Disease burden varied substantially by geographic region, with some states experiencing significantly higher incidence than others. The top 10 states with the highest total disease incidence are summarized in Table 2 . South Dakota and North Dakota had the highest reported total incidence, driven primarily by recurring outbreaks of West Nile Virus. Other high-burden states included Colorado, Idaho, and Nebraska. Notably, Maryland appeared among the top-ranked states. The spatial variation in incidence rates is further illustrated in Fig. 2. Table 2 Top 10 states ranked by total disease incidence. Rank State Total Incidence 1 South Dakota 3,190.4 2 North Dakota 2,819.9 3 Colorado 1,756.5 4 Maryland 1,666.7 5 Idaho 1,515.9 6 Nebraska 1,466.8 7 Wyoming 1,403.0 8 California 1,264.3 9 Montana 1,015.2 10 Kansas 995.7 3. Seasonality and Trend Analysis 3.1 Seasonality of Dengue The STL decomposition for Dengue is illustrated in Fig. 4 . The decomposition reveals a strong seasonal component, with incidence peaking at regular intervals each year, suggesting a predictable transmission cycle. The trend component indicates a gradual increase in Dengue incidence over time, particularly after the 1980s. Analysis of the seasonal component shows that Dengue follows a highly structured cycle, with peak incidence typically occurring during the late summer and early fall months, aligning with warm, humid conditions favorable for mosquito breeding. However, the remainder component suggests periodic unpredictable surges. 3.2 Seasonality of Malaria For Malaria, the STL decomposition in Fig. 5 highlights a strong seasonality, with a regular pattern of increased cases in the summer months. The long-term trend shows a significant decline in Malaria incidence, especially after the 1950s. Despite this downward trend, sporadic cases continue to appear. The seasonal peak remains consistent. 3.3 Seasonality of West Nile Virus The STL decomposition for West Nile Virus is presented in Fig. 6 . Unlike Malaria and Dengue, West Nile Virus exhibits less consistent seasonality, with some years showing sharp outbreaks followed by periods of minimal incidence. The trend component indicates a notable increase in cases in the early 2000s. The seasonal component remains strong, with peak transmission occurring in late summer and early fall. However, the remainder component suggests that West Nile Virus outbreaks are more unpredictable. 4. Climate Correlation Analysis 4.1 Spearman Correlation Between Climate and Disease Incidence Spearman correlation coefficients were computed to measure the strength and direction of the association between temperature, precipitation, and disease incidence. The results are presented in Table 7 . Table 7 Spearman correlation between climate variables and incidence. Disease Climatic Variable Spearman’s ρ 95% CI (Lower - Upper) P-value Dengue Precipitation -0.49 -0.58; -0.39 < 0.001 Temperature -0.28 -0.39; -0.16 < 0.001 Malaria Precipitation -0.21 -0.23; -0.19 < 0.001 Temperature -0.30 -0.32; -0.27 < 0.001 WNV Precipitation -0.43 -0.49; -0.37 < 0.001 Temperature -0.32 -0.38; -0.25 < 0.001 Spearman’s rank correlation analysis was conducted to evaluate the association between climate variables and the incidence of Dengue, Malaria, and West Nile Virus (WNV). The results revealed a moderate and statistically significant negative correlation between precipitation and Dengue incidence (ρ = -0.49, 95% CI: -0.58 to -0.39, p < 0.001), suggesting that periods of lower precipitation were associated with increased Dengue cases. Additionally, a weaker but significant negative correlation was observed between Dengue incidence and Temperature (ρ = -0.28, 95% CI: -0.39 to -0.16, p < 0.001). For Malaria, a significant but weak negative correlation was identified between precipitation and incidence (ρ = -0.21, 95% CI: -0.23 to -0.19, p < 0.001), while Temperature exhibited a stronger negative correlation with Malaria incidence (ρ = -0.30, 95% CI: -0.32 to -0.27, p < 0.001). The correlation analysis for West Nile Virus demonstrated a moderate negative correlation between precipitation and incidence (ρ = -0.43, 95% CI: -0.49 to -0.37, p < 0.001). Similarly, a negative correlation between Temperature and WNV incidence (ρ = -0.32, 95% CI: -0.38 to -0.25, p < 0.001) was observed. The CCF plots reveal significant negative correlations at lags of 0 to -6 months for Dengue and West Nile Virus, reinforcing that temperature changes influence disease incidence within a six-month period. Malaria showed no significant lagged effects (SUPPLEMENTARY MATERIAL). 5. Generalized Additive Models (GAMs) GAMs were applied to capture nonlinear effects of climate variables on disease incidence. The results demonstrated strong associations between climate and disease trends (Table 8). For Dengue, neither temperature (p = 0.953) nor precipitation (p = 0.212) showed significant effects. The adjusted R² remained low at 0.0088. For Malaria, both temperature and precipitation were highly significant (p < 0.001), with a higher adjusted R² (0.0836), suggesting that climate variation explains a larger proportion of malaria incidence. For West Nile Virus, precipitation was significant (p < 0.001), while temperature was not (p = 0.766). The model explained 3.92% of the variance in WNV incidence. Table 9 Summary of Generalized Additive Models (GAMs) Disease Variable edf Ref.df F-statistic p-value Adjusted R² Dengue s(Temperature) 1.000 1.00 0.003 0.953 0.0088 s(Precipitation) 1.351 1.63 1.226 0.212 Malaria s(Temperature) 8.435 8.88 8.702 < 0.001 0.0836 s(Precipitation) 7.243 8.26 47.384 < 0.001 West Nile Virus s(Temperature) 1.431 1.75 0.316 0.766 0.0392 s(Precipitation) 7.041 8.10 4.129 < 0.001 6. Forecasting Analysis: ARIMAX Model Dengue ARIMAX Model The ARIMAX (0,0,0) (0,12,0) model was identified as the best-fitting model for Dengue incidence. The Akaike Information Criterion (AIC) was 1881.25, and the Bayesian Information Criterion (BIC) was 1895.41, with a log-likelihood of -936.62. This suggests that a seasonal pattern with a 12-month cycle is the primary driver of fluctuations in Dengue incidence, with limited influence from autoregressive or moving average components. The model projected cases rising from 253 cases to 379 cases by late 2026. Temperature was a significant predictor (p = 0.008), while precipitation had a weaker effect (p = 0.045). The 95% confidence interval (CI) ranged from 150 to 525 cases (Fig. 4 ). For Malaria incidence, the best-fitting model was ARIMAX(1,2,0)(2,12,0). The model’s AIC was 56,649.89, and the BIC was 56,712.91, with a log-likelihood of -28,315.95. This suggests that Malaria incidence follows a more complex seasonal pattern, with significant autoregressive and differencing components necessary to capture long-term trends and periodic variation. Incidence was forecasted to peak in wet months, reaching up to 156 cases by the end of 2026, with a 95% CI of 55 to 210 cases (Fig. 17). Precipitation had a strong effect (p < 0.001), while temperature was only marginally significant (p = 0.072). The best-fitting ARIMAX(2,2,0)(0,12,0) model for West Nile Virus incidence had an AIC of 8615.65, a BIC of 8652.76, and a log-likelihood of -4299.83. The presence of two autoregressive and two differencing components suggests that WNV incidence follows a long-term increasing or decreasing trend, with seasonal variations occurring at a 12-month interval. Forecasts showed periodic outbreaks, with incidence fluctuating between 17 to 32 cases, and peak months exceeding 50 cases (Fig. 18). Precipitation had a strong negative effect (p = 0.002), while temperature was not significant (p = 0.36). Discussion This study provides a comprehensive analysis of the incidence, geographic distribution, seasonality, and climatic associations of dengue, malaria, and WNV in the U.S. over multiple decades. These results, based on the fact that climate change presents significant risks to human health and heightens the likelihood of emerging diseases in the coming years[ 16 ], help understand how climatic factors influence vector-borne diseases, predict outbreaks, and implement effective control measures[ 17 ]. Incidence Patterns and Disease Burden WNV had the highest mean incidence and showed the greatest variability, indicating that outbreaks are sporadic yet severe, likely influenced by mosquito population dynamics, bird reservoirs, and climate fluctuations. The presence of extreme outliers in WNV incidence, as highlighted in the log-scale visualization, reinforces the idea that large-scale outbreaks occur intermittently rather than through sustained endemic transmission. Studies have found a clear association between extreme heat events and heightened outbreak intensity in human populations[ 18 ], which goes in line with our study findings. These results underscore the role of climate variability as a key driver of vector-borne disease dynamics, where temperature fluctuations may not maintain continuous transmission but instead create conditions that trigger sudden, large-scale outbreaks when critical environmental thresholds are reached[ 19 ]. Our study findings indicate that dengue has the lowest mean incidence but experiences periodic spikes, suggesting that transmission is primarily episodic rather than sustained. According to the CDC, dengue in the U.S. mainly affects travelers returning from endemic regions, with only sporadic outbreaks of locally acquired cases[ 20 ], which aligns with our findings. In contrast, malaria had the highest overall case count but a low median monthly incidence, reflecting its historical prevalence in the U.S. before successful eradication efforts. While sporadic cases continue to be reported, these are likely imported rather than locally transmitted. According to the CDC, locally acquired mosquito-borne malaria has not occurred in the U.S. since 2003, when a small outbreak of Plasmodium vivax malaria was identified in Florida[ 21 , 22 ]. Despite this, the overall risk of local transmission remains extremely low[ 21 ]. However, Anopheles mosquitoes, which are present across many regions, remain capable of transmitting malaria if they bite an infected individual. The risk is highest in areas where climate conditions allow these vectors to survive year-round and where travelers from malaria-endemic regions are present[ 21 ], aligning with our study’s findings. Geographic Distribution of Vector-Borne Diseases in the United States The geographic distribution of disease burden further illustrates these trends, with North Dakota (2,819.9 cases), South Dakota (3,190.4 cases), Colorado (1,756.5 cases), and Nebraska (1,466.8 cases) ranking among the highest-incidence states—primarily due to recurrent WNV outbreaks. WNV is most prevalent in these states because warm summers and drier conditions create favorable environments for Culex mosquito populations, promoting virus amplification and transmission[ 19 ]. In contrast, dengue remains concentrated in southern regions, where Aedes aegypti and Aedes albopictus thrive in warm, humid environments with frequent rainfall, facilitating periodic outbreaks. This pattern aligns with Chen et al.’s study, which identified similar climatic drivers of dengue transmission[ 23 ]. Meanwhile, malaria cases in the U.S. remain sporadic and primarily linked to travel-related introductions, despite the presence of Anopheles mosquito vectors in many regions[ 21 ]. The relatively low risk of malaria transmission is likely due to effective vector control programs, shorter mosquito lifespans in temperate climates, and limited exposure to infected individuals needed to sustain transmission cycles[ 21 ]. Seasonality and Long-Term Trends in Vector-Borne Disease Transmission WNV exhibited high variability in incidence but did not follow a consistent seasonal pattern. While WNV transmission peaks during warm months, its outbreak timing remains unpredictable due to several ecological and environmental factors[ 24 ]. Unlike dengue, which primarily relies on Aedes mosquitoes, WNV transmission depends on both Culex mosquito vectors and bird reservoir hosts[ 24 ]. Variability in bird migration, and mosquito abundance can lead to fluctuating transmission patterns rather than predictable seasonal peaks[ 24 ]. Additionally, climatic anomalies such as droughts and extreme heat events play a critical role in WNV outbreaks[ 24 ]. Research has shown that higher temperatures accelerate viral replication within mosquitoes, while drought conditions may increase transmission by concentrating bird populations and reducing water sources, forcing birds and mosquitoes into closer contact[ 24 ]. This pattern aligns with our findings, where WNV incidence shows episodic surges rather than a strict seasonal trend. Dengue follows a strong seasonal pattern, peaking in late summer and early fall, driven by elevated temperatures and increased precipitation that enhance Aedes mosquito populations and transmission risk which aligns with prior research studies[ 23 ]. For Malaria, the STL decomposition highlights a strong seasonality, with a regular pattern of increased cases in the summer months. This seasonal trend aligns with the known life cycle of Anopheles mosquitoes, which are more active and reproduce more efficiently in warm temperatures[ 25 ]. WNV incidence spiked in the early 2000s before declining, likely due to vector control efforts, and enhanced surveillance measures. Following its introduction in 1999, significant U.S. government investments in vector-borne disease monitoring and research led to notable short-term improvements in surveillance, contributing to better outbreak detection and response[ 26 ]. Dengue has steadily increased since the 1980s, driven by climate change, urbanization, and travel-related introductions. This issue is further compounded by changing environmental and ecological conditions that enhance dengue vector persistence[ 27 ], along with increased international travel contributing to case importation[ 28 ]. In contrast, malaria has declined, reflecting effective vector control and minimal local transmission[ 21 ]. Impact of Climate Variability on Vector-Borne Disease Incidence Spearman’s correlation analysis revealed that all three diseases—WNV, dengue, and malaria—exhibited significant negative correlations with both precipitation and temperature. For WNV, dry conditions and lower precipitation levels were associated with increased transmission, while temperature variations modulated the transmission patterns. Similarly, lower precipitation levels correlated with increased dengue incidence, likely due to the accumulation of stagnant water in urban environments that favors mosquito breeding, while extreme temperatures may have disrupted virus replication[ 29 ]. In contrast to other studies that showed that dengue thrives in humid weather[ 29 ], this finding suggests that local environmental conditions and human-driven water storage practices may also play a significant role in disease transmission. While WNV benefits from dry conditions due to increased vector-host interactions[ 30 ], dengue incidence may rise during dry periods because water storage practices create artificial breeding sites for Aedes mosquitoes[ 29 ]. Malaria showed a weaker but statistically significant negative correlation with both precipitation and temperature, suggesting that excessive rainfall could wash away breeding sites, reducing transmission potential, while lower temperatures might slow parasite development[ 29 ]. Temporal Dynamics and Lag Effects Cross-correlation analysis further elucidated the temporal relationships between climate variables and disease incidence. Significant negative correlations at lags of 0 to -6 months for WNV and dengue reinforce the notion that temperature and precipitation fluctuations influence disease transmission within a six-month window. Malaria, however, did not exhibit significant lagged effects, suggesting a more complex interplay between climatic and non-climatic factors. Nonlinear Associations and Predictive Modeling Generalized Additive Models (GAMs) were employed to assess the nonlinear relationships between climate variables and disease incidence, revealing varying levels of association across the three vector-borne diseases studied. For WNV, precipitation emerged as a significant predictor (p < 0.001), while temperature did not (p = 0.766). The adjusted R² value of 0.0392 suggests that precipitation accounts for a modest proportion of WNV variability. The negative association with precipitation is consistent with previous research indicating that dry conditions favor WNV transmission by reducing water availability, leading to increased bird-mosquito interactions in concentrated water sources[ 31 ]. The absence of a significant temperature effect may reflect the complex ecological interplay between Culex mosquitoes, avian hosts, and environmental conditions, suggesting that factors such as humidity, land use, and vector-host dynamics may play a more prominent role than temperature alone[ 32 ]. For dengue, neither temperature (p = 0.953) nor precipitation (p = 0.212) showed significant effects, with a notably low adjusted R² of 0.0088. This suggests that climate variability alone does not sufficiently explain dengue incidence patterns in the study area. The lack of association may be due to the predominant role of other environmental and socio-ecological factors, such as urbanization, vector control measures, and human movement, which influence Aedes mosquito populations and dengue transmission. Prior studies have indicated that while temperature and precipitation play roles in dengue epidemiology, their impact is often modulated by additional determinants such as water storage practices and artificial breeding sites[ 33 ]. In contrast, malaria incidence exhibited a strong relationship with climate variability. Both temperature and precipitation were highly significant predictors (p < 0.001), with an adjusted R² of 0.0836. This finding aligns with the well-documented ecological dependencies of Anopheles mosquitoes, whose reproductive cycles and parasite development rates are directly influenced by climatic conditions[ 25 ]. Forecasting Trends and Implications ARIMAX modeling demonstrated that climate variables contribute to the seasonal and long-term trends of disease incidence. The best-fitting ARIMAX(2,2,0)(0,12,0) model for WNV incidence suggested periodic outbreaks, with precipitation playing a significant role (p = 0.002), likely due to its impact on mosquito breeding sites and bird-mosquito interactions, which influence disease transmission dynamics. This aligns with findings from Paz and Semenza, who reported that drier conditions facilitate WNV outbreaks by concentrating mosquito populations and increasing contact with avian hosts[ 31 ]. Dengue incidence exhibited a strong seasonal component with a 12-month cycle, with temperature emerging as a significant predictor (p = 0.008), consistent with findings from Messina et al., who demonstrated that dengue transmission increases with higher temperatures due to accelerated mosquito development and virus replication[ 33 ]. Malaria incidence exhibited a complex seasonal pattern, peaking during wet months, with precipitation exerting a dominant influence (p < 0.001). This is consistent with the findings of Caminade et al., who reported that increased precipitation enhances mosquito replication and malaria transmission[ 25 ]. These projections emphasize the need for proactive vector control and climate adaptation strategies to mitigate disease outbreaks. Public Health Implications and Future Directions The findings from this study underscore the need for climate-adaptive disease surveillance and vector control strategies. WNV outbreaks may intensify under future climate scenarios characterized by increased droughts and heatwaves, necessitating targeted interventions in high-risk states. Dengue’s sporadic yet increasing presence in the U.S. warrants strengthened monitoring, particularly in regions with growing Aedes mosquito populations. Malaria remains a low-risk threat, but the presence of competent vectors in many areas highlights the importance of maintaining strong public health infrastructure to prevent local transmission. Limitations While this study provides valuable insights into the relationship between climate variability and vector-borne diseases, certain limitations must be acknowledged. First, the reliance on aggregated population-level data limits the ability to infer individual-level causality, as factors such as human movement and socioeconomic conditions were not accounted for. Second, variations in disease reporting practices across states and periods may introduce inconsistencies in data quality, potentially affecting trend interpretations. Lastly, this study does not account for the potential effects of vector control measures, or socioeconomic factors, which can influence disease transmission independently of climate variables. Conclusion This study confirms that WNV exhibits the highest incidence and variability, while dengue and malaria remain lower in prevalence but follow distinct transmission patterns. The geographic distribution of disease burden highlights that WNV is concentrated in drier, high-risk states, whereas dengue is localized to southern humid regions, and malaria remains sporadic. Seasonality analysis reinforces that WNV does now follow a seasonal pattern, whereas, dengue follows a seasonal pattern peak in late summer and early fall, and malaria follows a consistent summer peak. Climate variability plays a crucial role in shaping these patterns, with drier conditions amplifying WNV outbreaks while fluctuating precipitation and temperature contribute to dengue and malaria transmission dynamics. Future research should integrate additional environmental and socio-ecological factors to refine predictive models and improve public health interventions. Enhanced surveillance, climate-informed vector control strategies, and early warning systems will be critical in adapting to the changing epidemiology of vector-borne diseases in a warming world. Declarations Ethics Approval and Consent to Participate. This study utilized publicly available data from Project Tycho and PRISM. Data is deidentified. No ethical approval was required for this study, Consent for publication: Not applicable. Availability of data and materials : Available as part of Project Tycho and PRISM databases. Competing interests: The authors have nothing to disclose. Funding: None. Author contributions: Ali Hemade: Conceptualization, Methodology, Data Curation, Data Collection, Statistical Analysis, Formal Analysis, Writing – Original Draft (Methods and Results). Writing – Review and Editing. Maria Akiki: Writing – Original Draft (Introduction and Discussion). Pascale Salameh: Supervision, Writing – Review & Editing. References Pielke Jr RA: What is climate change? Energy & environment 2004, 15 (3):515-520. Field CB, Barros VR: Climate change 2014–Impacts, adaptation and vulnerability: Regional aspects : Cambridge University Press; 2014. 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Mojahed N, Mohammadkhani MA, Mohamadkhani A: Climate crises and developing vector-borne diseases: a narrative review . Iranian journal of public health 2022, 51 (12):2664. Gubler DJ: 1 Vector-Borne Disease Emergence and Resurgence . 2008. Bansal V, Munjal J, Lakhanpal S, Gupta V, Garg A, Munjal RS, Jain R: Epidemiological shifts: The emergence of malaria in America . In: Baylor University Medical Center Proceedings: 2023 : Taylor & Francis; 2023: 745-750. Sejvar JJ: West Nile virus: an historical overview . Ochsner Journal 2003, 5 (3):6-10. Van Panhuis W, Cross A, Burke D: Counts of Disease caused by West Nile virus reported in UNITED STATES OF AMERICA: 2002-2005: (version 2.0, April 1, 2018) . In . Van Panhuis W, Cross A, Burke D: Counts of Malaria reported in UNITED STATES OF AMERICA: 1951-2017: (version 2.0, April 1, 2018) . In . Van Panhuis W, Cross A, Burke D: Counts of Dengue reported in UNITED STATES OF AMERICA: 1924-2017: (version 2.0, April 1, 2018) . In . 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Control CfD, Prevention: Locally acquired malaria cases identified in the United States . CDC Health Alert Network 2023. Malecki J, Kumar S, Johnson B, Gidley M: Local transmission of Plasmodium vivax malaria-palm Beach County, Florida, 2003 . MMWR: Morbidity & Mortality Weekly Report 2003, 52 (38):908-908. Chen LH, Marti C, Diaz Perez C, Jackson BM, Simon AM, Lu M: Epidemiology and burden of dengue fever in the United States: a systematic review . Journal of Travel Medicine 2023, 30 (7). Edition F: Climate change indicators in the united states, 2016 . 2016. Caminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González FJ, Stenlund H, Martens P, Lloyd SJ: Impact of climate change on global malaria distribution . Proceedings of the National Academy of Sciences 2014, 111 (9):3286-3291. Petersen LR, Nasci R, Beard CB, Massung R: Emerging Vector-Borne diseases in the United States: What is next, and are we prepared . In: Global Health Impacts of Vector-Borne Diseases: Workshop Summary: 2016 : National Academies Press (US); 2016. San Martín JL, Brathwaite O, Zambrano B, Solórzano JO, Bouckenooghe A, Dayan GH, Guzmán MG: The epidemiology of dengue in the Americas over the last three decades: a worrisome reality . The American journal of tropical medicine and hygiene 2010, 82 (1):128. Wilder-Smith A, Schwartz E: Dengue in travelers . New England journal of medicine 2005, 353 (9):924-932. Franklinos LH, Jones KE, Redding DW, Abubakar I: The effect of global change on mosquito-borne disease . The Lancet infectious diseases 2019, 19 (9):e302-e312. Reisen WK, Fang Y, Martinez VM: Effects of temperature on the transmission of West Nile virus by Culex tarsalis (Diptera: Culicidae) . Journal of medical entomology 2006, 43 (2):309-317. Paz S, Semenza JC: Environmental drivers of West Nile fever epidemiology in Europe and Western Asia—a review . International journal of environmental research and public health 2013, 10 (8):3543-3562. Kilpatrick AM, Meola MA, Moudy RM, Kramer LD: Temperature, viral genetics, and the transmission of West Nile virus by Culex pipiens mosquitoes . PLoS pathogens 2008, 4 (6):e1000092. Messina JP, Brady OJ, Pigott DM, Golding N, Kraemer MU, Scott TW, Wint GW, Smith DL, Hay SI: The many projected futures of dengue . Nature Reviews Microbiology 2015, 13 (4):230-239. Additional Declarations No competing interests reported. Supplementary Files Suppl.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6208975\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":427617418,\"identity\":\"73dee875-6b20-4e33-ba05-47f74dd2b4e0\",\"order_by\":0,\"name\":\"Ali Hemade\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdsY2EMXYwN98AEhLyBDWwgzTInEsAaSFhwgtDGwQLQw5BiAGYS38zcxtDz7m2Mn2M5z5/OpGjQUPA/vhoxvwaZE4zNhuOHNbsvHM5t5t1jnHgA7jSUu7gdeaw4xt0rzbmBM3HDi7zTiHDahFgscMrxZ5kJa/2+oT9x/IeWac848ILQYgLYzbDiduYMhhfpzbRoQWQ6AWyd5tx41n3DhmxpzbJ8HDRsgvcsfbn0n83FYt29/f/Phzzrc6OX72w8fwex8JsEmASWKVgwDzB1JUj4JRMApGwcgBACr6SekvNCM5AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Lebanese University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ali\",\"middleName\":\"\",\"lastName\":\"Hemade\",\"suffix\":\"\"},{\"id\":427617419,\"identity\":\"69688b38-09e6-4c8b-8241-796eb2b6fa14\",\"order_by\":1,\"name\":\"Maria Akiki\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Connecticut\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Maria\",\"middleName\":\"\",\"lastName\":\"Akiki\",\"suffix\":\"\"},{\"id\":427617420,\"identity\":\"ff682622-9efc-4c03-9a4e-393257359994\",\"order_by\":2,\"name\":\"Pascale Salameh\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Lebanese University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pascale\",\"middleName\":\"\",\"lastName\":\"Salameh\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-03-12 06:08:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6208975/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6208975/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":78797179,\"identity\":\"dc8bcf2e-9d4a-4b02-b811-e6adf4cf20ca\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:38:19\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":146796,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBoxplot of disease incidence (log scale) - \\u003c/strong\\u003eThis box plot displays the distribution of incidence rates for Dengue, Malaria, and West Nile Virus (WNV) on a logarithmic scale. The central line in each box represents the median incidence, while the upper and lower edges correspond to the interquartile range (IQR). The whiskers extend to 1.5 times the IQR, with points beyond this range considered outliers.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/b89a7c9748a717356537308d.png\"},{\"id\":78798540,\"identity\":\"5a4750d4-d7eb-4e3b-bc17-13f216283e67\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 06:02:29\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":240774,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 3 – Boxplot for disease incidence for the top 10 states\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/106fe99a31a370caf4a73e6c.png\"},{\"id\":78796752,\"identity\":\"8ed36dd3-4bb4-4d5b-bf69-314180cf32d5\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:30:19\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":423435,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 4. STL decomposition of Dengue incidence. \\u003c/strong\\u003eSeasonal-Trend Decomposition (STL) of Dengue incidence over time, showing the observed data (top), seasonal component (second panel), trend component (third panel), and remainder (bottom panel). The seasonal pattern demonstrates periodic fluctuations, while the trend indicates a gradual increase in cases, with a sharp spike in recent years. The remainder captures unexplained variations and anomalies.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/fa680483f86574ed1b4cb57b.png\"},{\"id\":78796734,\"identity\":\"48f1887f-b336-42e0-a2ec-5b27169e5c69\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:30:19\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":401681,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 5. STL decomposition of Malaria incidence. \\u003c/strong\\u003eSeasonal-Trend Decomposition (STL) of Malaria incidence over time, illustrating the observed data (top), seasonal component (second panel), trend component (third panel), and remainder (bottom panel). The seasonal component highlights periodic fluctuations, while the trend component shows long-term variations, including a peak in cases followed by a decline in recent years. The remainder captures irregular variations and anomalies in the data.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/1edb8717498b864c8cc4ac42.png\"},{\"id\":78797183,\"identity\":\"2b1b5cf5-6ac7-4f56-992e-ad489a2698a9\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:38:19\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":382843,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 6. STL decomposition of West Nile Virus incidence. \\u003c/strong\\u003eSeasonal-Trend Decomposition (STL) of West Nile Virus incidence over time, displaying the observed data (top panel), seasonal component (second panel), trend component (third panel), and remainder (bottom panel). The seasonal component highlights annual fluctuations in incidence, while the trend component captures long-term variations, including peaks in incidence around the mid-2000s followed by a gradual decline. The remainder reflects irregular variations and short-term anomalies in the data.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/0b3eb2c2ea0397d2e974a433.png\"},{\"id\":78796722,\"identity\":\"444f96c6-6eea-4bd5-98b8-89d28b11a3c9\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:30:18\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":114025,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 7: ARIMAX Forecast for Dengue Incidence. \\u003c/strong\\u003eFinal ARIMAX(0,0,0)(0,12,0) model forecast for Dengue incidence over the next 10 years, incorporating precipitation and temperature as exogenous predictors. The blue line represents the predicted incidence per 100,000 population, while the shaded region denotes the 95% confidence interval. The model captures strong seasonal variations, with uncertainty increasing over time.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/4f93cdc85ad4e8c9001b3a5e.png\"},{\"id\":78797185,\"identity\":\"7b6afc16-c343-4b4b-9902-addf429f0a8b\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:38:19\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":138551,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 8: ARIMAX Forecast for Malaria Incidence. \\u003c/strong\\u003eFinal ARIMAX(1,2,0)(2,12,0) model forecast for Malaria incidence over the next 10 years, incorporating precipitation and temperature as exogenous predictors. The blue line represents the predicted incidence per 100,000 population, while the shaded region denotes the 95% confidence interval. The model reflects the historical fluctuations in malaria incidence and projects a stabilization trend, though uncertainty increases over time.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/aa6eddf0e3a8c526a4dea61d.png\"},{\"id\":78797187,\"identity\":\"e92a058d-dc3d-41ab-b190-a42c4b065684\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:38:19\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":118156,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFigure 9: ARIMAX Forecast for West Nile Virus Incidence. \\u003c/strong\\u003eFinal ARIMAX(2,2,0)(0,12,0) model forecast for West Nile Virus incidence over the next 10 years, integrating precipitation and temperature as exogenous predictors. The blue line represents the predicted incidence per 100,000 population, while the shaded region indicates the 95% confidence interval. The model suggests a stabilization of incidence; however, the wide confidence intervals reflect high uncertainty in long-term projections.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/7da4bb324e91dd22b5225498.png\"},{\"id\":87524511,\"identity\":\"1e5917ce-1d92-4cef-94e8-455308416e9b\",\"added_by\":\"auto\",\"created_at\":\"2025-07-24 19:01:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4258190,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/13416e87-fc9b-405b-a1b2-5dd156d695c8.pdf\"},{\"id\":78797177,\"identity\":\"43ad879f-b76c-4290-b946-26e55325f1ca\",\"added_by\":\"auto\",\"created_at\":\"2025-03-19 05:38:19\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":900328,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Suppl.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6208975/v1/a0f71f7960c1527b7edf9dc2.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Climate Variability and Vector-Borne Disease Dynamics: A Time-Series Analysis of Dengue, Malaria, and West Nile Virus in the United States\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eClimate change refers to the prolonged alteration of Earth's typical weather patterns at local, regional, and global levels[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Since 1900, the global average temperature has risen by 1.1\\u0026deg;C, with most of this warming occurring over the past 50 years[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Rising temperatures and other consequences of climate change such as shifting precipitation patterns have a profound impact on vector-borne diseases by altering the survival, distribution, and behavior of disease-causing organisms, the carriers that transmit them, and the susceptible populations they infect[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The dynamic relationship between temperature, vectors, and pathogens can influence the likelihood of disease transmission between humans and the potential spillover of infections from animal reservoirs to human populations[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. As a result, climate change has already created favorable conditions for the transmission of various infectious diseases, including Lyme disease, West Nile Virus (WNV), dengue, and malaria[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Fleas and ticks thrive in warmer seasons, while malaria remains endemic year-round, with rising tick populations likely linked to increasingly mild winters[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. As climate change accelerates, understanding these relationships is crucial for forecasting outbreaks and developing effective public health strategies[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn the United States (U.S.), dengue, malaria, and WNV represent key mosquito-borne diseases with distinct epidemiological patterns[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Dengue, historically endemic to tropical regions, has been reported with increasing frequency in southern U.S. states, coinciding with warming temperatures and changing precipitation patterns[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Malaria, once widespread in the U.S., has been largely eliminated as a locally transmitted disease but remains a concern due to imported cases and changing environmental conditions that could favor vector resurgence[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. WNV, endemic to North America since its introduction in 1999, exhibits periodic outbreaks, particularly in warmer and drier conditions that facilitate mosquito breeding and viral transmission[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite growing evidence linking climate variability to vector-borne disease transmission, few studies have conducted long-term time-series analyses to assess these relationships at a national scale. This study employs a retrospective ecological time-series approach to examine the association between temperature, precipitation, and the incidence of dengue, malaria, and WNV in the United States. By analyzing disease patterns over several decades, this study seeks to uncover the impact of climate variability on transmission dynamics and provide insights that can enhance early warning systems, vector control interventions, and public health response strategies.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eThis study employed a retrospective ecological time-series analysis to investigate the association between climate variables and the incidence of Dengue, Malaria, and West Nile Virus (WNV) in the United States. The dataset included state-level, monthly disease incidence rates expressed as new cases per 100,000 population and corresponding climatic data, specifically precipitation and temperature. Data were sourced from publicly available epidemiological and meteorological databases spanning several decades, allowing for an extensive temporal assessment of disease trends and their potential climatic drivers.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Population and Data Collection\\u003c/h2\\u003e \\u003cp\\u003eDisease incidence data were obtained from \\u003cb\\u003eProject Tycho\\u003c/b\\u003e [\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] a comprehensive epidemiological database that compiles historical records of infectious diseases in the United States. The dataset contained monthly confirmed case reports of Dengue, Malaria, and WNV across multiple states. To ensure consistency in reporting and comparability across different states and time periods, incidence values, standardized to reflect cases per 100,000 population, were used.\\u003c/p\\u003e \\u003cp\\u003eClimatic data were retrieved from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) database [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], which provides high-resolution meteorological data across the United States. Monthly temperature and precipitation values were extracted and linked to disease incidence records based on state and month of reporting. The final dataset was structured to include variables for disease incidence, climate metrics, and temporal identifiers (year and month).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eTo describe trends in disease incidence and climate variability, summary statistics were computed for each disease, including measures of central tendency and dispersion. The geographic distribution of disease burden was assessed by ranking the top ten states with the highest cumulative incidence. Data visualization techniques, including histograms and boxplots, were employed to examine the distribution of incidence rates across different states and over time.\\u003c/p\\u003e \\u003cp\\u003eTime series decomposition was performed using Seasonal-Trend decomposition via Loess (STL) to separate observed incidence into seasonal, trend, and remainder components. The seasonal component captured recurring annual patterns in disease incidence, while the trend component represented long-term changes. The remainder component accounted for irregular fluctuations unexplained by seasonal and trend components.\\u003c/p\\u003e \\u003cp\\u003eTo assess the association between climate variables and disease incidence, Spearman\\u0026rsquo;s rank correlation was used. This non-parametric method was selected due to its robustness against non-linear relationships and non-normally distributed data. Correlation coefficients, along with 95% confidence intervals and p-values, were computed to determine the strength and statistical significance of associations between precipitation, temperature, and disease incidence. Additionally, cross-correlation function (CCF) analysis was conducted to explore potential lead-lag relationships, identifying whether changes in climatic conditions preceded changes in disease incidence or vice versa.\\u003c/p\\u003e \\u003cp\\u003eTo model the relationship between climate variables and disease incidence while accounting for non-linear effects, Generalized Additive Models (GAMs) were fitted separately for each disease. These models included smoothed functions for temperature and precipitation, allowing for flexible modeling of non-linear associations. The statistical significance of these terms was evaluated using F-tests, and model fit was assessed through adjusted R-squared values and generalized cross-validation scores.\\u003c/p\\u003e \\u003cp\\u003eFor time series forecasting, an Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model was developed for each disease. The ARIMAX approach incorporated autoregressive, differencing, and moving average components while integrating climate variables as external regressors. The optimal model parameters were selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Forecasting was performed for a ten-year period, with prediction intervals generated to quantify uncertainty.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eEthical Considerations\\u003c/h3\\u003e\\n\\u003cp\\u003eThis study utilized publicly available, de-identified epidemiological and climate datasets, ensuring that no individual-level data were accessed or analyzed. Given the ecological design and reliance on aggregated historical data, the study did not require ethical approval as per institutional and regulatory guidelines. Data handling and analysis were conducted in compliance with best practices for research integrity and reproducibility.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1. Data Summary and Descriptive Statistics\\u003c/h2\\u003e \\u003cp\\u003eThe dataset consists of multiple years (1925 to 2017) of monthly disease incidence records for Dengue, Malaria, and West Nile Virus, covering all U.S. states. Each record includes disease incidence counts and incidence per 100,000 population alongside climate variables, specifically temperature and precipitation.\\u003c/p\\u003e \\u003cp\\u003eDescriptive statistics for disease incidence across the entire population are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. West Nile Virus exhibited the highest mean incidence, averaging 19.32 cases per month with substantial variability (SD\\u0026thinsp;=\\u0026thinsp;69.59). Malaria had the highest overall case count in the dataset, yet its median monthly incidence was relatively low (0.085 cases). Dengue showed the lowest mean incidence but had months where cases peaked at 151, reflecting its tendency for sporadic outbreaks in limited regions. The median incidence for all diseases was lower than the mean, confirming that the data distribution is highly skewed, with occasional extreme outbreaks rather than sustained transmission across months.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDescriptive statistics of disease incidence (1925 to 2017) in the total population.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDisease\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCount\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean Incidence\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMin\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMax\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDengue\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e255\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.62\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e151.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMalaria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8,120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2.59\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e240.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.085\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWest Nile Virus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e764\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e19.32\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e69.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0026\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e923.48\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2.42\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe overall distribution of disease incidence is visualized in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. West Nile Virus showed the widest spread of values. In contrast, Dengue and Malaria had relatively lower median values, but right-skewed distributions. The application of a logarithmic scale in the visualization helped clarify the incidence differences across diseases, particularly highlighting the presence of extreme outliers in West Nile Virus cases.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2. Geographic Distribution of Disease Incidence\\u003c/h2\\u003e \\u003cp\\u003eDisease burden varied substantially by geographic region, with some states experiencing significantly higher incidence than others. The top 10 states with the highest total disease incidence are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. South Dakota and North Dakota had the highest reported total incidence, driven primarily by recurring outbreaks of West Nile Virus. Other high-burden states included Colorado, Idaho, and Nebraska. Notably, Maryland appeared among the top-ranked states. The spatial variation in incidence rates is further illustrated in Fig.\\u0026nbsp;2.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eTop 10 states ranked by total disease incidence.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRank\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eState\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTotal Incidence\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSouth Dakota\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3,190.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNorth Dakota\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,819.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eColorado\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,756.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaryland\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,666.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdaho\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,515.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNebraska\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,466.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWyoming\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,403.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCalifornia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,264.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMontana\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,015.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKansas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e995.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e3. \\u003cb\\u003eSeasonality and Trend Analysis\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e3.1 Seasonality of Dengue\\u003c/h3\\u003e\\n\\u003cp\\u003eThe STL decomposition for Dengue is illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. The decomposition reveals a strong seasonal component, with incidence peaking at regular intervals each year, suggesting a predictable transmission cycle. The trend component indicates a gradual increase in Dengue incidence over time, particularly after the 1980s.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnalysis of the seasonal component shows that Dengue follows a highly structured cycle, with peak incidence typically occurring during the late summer and early fall months, aligning with warm, humid conditions favorable for mosquito breeding. However, the remainder component suggests periodic unpredictable surges.\\u003c/p\\u003e\\n\\u003ch3\\u003e3.2 Seasonality of Malaria\\u003c/h3\\u003e\\n\\u003cp\\u003eFor Malaria, the STL decomposition in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e highlights a strong seasonality, with a regular pattern of increased cases in the summer months. The long-term trend shows a significant decline in Malaria incidence, especially after the 1950s. Despite this downward trend, sporadic cases continue to appear. The seasonal peak remains consistent.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Seasonality of West Nile Virus\\u003c/h2\\u003e \\u003cp\\u003eThe STL decomposition for West Nile Virus is presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. Unlike Malaria and Dengue, West Nile Virus exhibits less consistent seasonality, with some years showing sharp outbreaks followed by periods of minimal incidence. The trend component indicates a notable increase in cases in the early 2000s. The seasonal component remains strong, with peak transmission occurring in late summer and early fall. However, the remainder component suggests that West Nile Virus outbreaks are more unpredictable.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4. Climate Correlation Analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.1 Spearman Correlation Between Climate and Disease Incidence\\u003c/h2\\u003e \\u003cp\\u003eSpearman correlation coefficients were computed to measure the strength and direction of the association between temperature, precipitation, and disease incidence. The results are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSpearman correlation between climate variables and incidence.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026minus;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDisease\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eClimatic Variable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSpearman\\u0026rsquo;s ρ\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95% CI (Lower - Upper)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDengue\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.58; -0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTemperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.39; -0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMalaria\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.23; -0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTemperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.32; -0.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWNV\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.49; -0.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTemperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.38; -0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSpearman\\u0026rsquo;s rank correlation analysis was conducted to evaluate the association between climate variables and the incidence of Dengue, Malaria, and West Nile Virus (WNV). The results revealed a moderate and statistically significant negative correlation between precipitation and Dengue incidence (ρ = -0.49, 95% CI: -0.58 to -0.39, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting that periods of lower precipitation were associated with increased Dengue cases. Additionally, a weaker but significant negative correlation was observed between Dengue incidence and Temperature (ρ = -0.28, 95% CI: -0.39 to -0.16, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eFor Malaria, a significant but weak negative correlation was identified between precipitation and incidence (ρ = -0.21, 95% CI: -0.23 to -0.19, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while Temperature exhibited a stronger negative correlation with Malaria incidence (ρ = -0.30, 95% CI: -0.32 to -0.27, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eThe correlation analysis for West Nile Virus demonstrated a moderate negative correlation between precipitation and incidence (ρ = -0.43, 95% CI: -0.49 to -0.37, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Similarly, a negative correlation between Temperature and WNV incidence (ρ = -0.32, 95% CI: -0.38 to -0.25, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was observed.\\u003c/p\\u003e \\u003cp\\u003eThe CCF plots reveal significant negative correlations at lags of 0 to -6 months for Dengue and West Nile Virus, reinforcing that temperature changes influence disease incidence within a six-month period. Malaria showed no significant lagged effects (SUPPLEMENTARY MATERIAL).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5. Generalized Additive Models (GAMs)\\u003c/h2\\u003e \\u003cp\\u003eGAMs were applied to capture nonlinear effects of climate variables on disease incidence. The results demonstrated strong associations between climate and disease trends (Table\\u0026nbsp;8).\\u003c/p\\u003e \\u003cp\\u003eFor Dengue, neither temperature (p\\u0026thinsp;=\\u0026thinsp;0.953) nor precipitation (p\\u0026thinsp;=\\u0026thinsp;0.212) showed significant effects. The adjusted R\\u0026sup2; remained low at 0.0088.\\u003c/p\\u003e \\u003cp\\u003eFor Malaria, both temperature and precipitation were highly significant (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with a higher adjusted R\\u0026sup2; (0.0836), suggesting that climate variation explains a larger proportion of malaria incidence.\\u003c/p\\u003e \\u003cp\\u003eFor West Nile Virus, precipitation was significant (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while temperature was not (p\\u0026thinsp;=\\u0026thinsp;0.766). The model explained 3.92% of the variance in WNV incidence.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 9\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of Generalized Additive Models (GAMs)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDisease\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eedf\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRef.df\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eF-statistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAdjusted R\\u0026sup2;\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDengue\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Temperature)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.953\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0088\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Precipitation)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.351\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.226\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.212\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMalaria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Temperature)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.435\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.702\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0836\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Precipitation)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.243\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e47.384\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWest Nile Virus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Temperature)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.431\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.316\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.766\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0392\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003es(Precipitation)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e6. Forecasting Analysis: ARIMAX Model\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eDengue ARIMAX Model\\u003c/h2\\u003e \\u003cp\\u003eThe ARIMAX (0,0,0) (0,12,0) model was identified as the best-fitting model for Dengue incidence. The Akaike Information Criterion (AIC) was 1881.25, and the Bayesian Information Criterion (BIC) was 1895.41, with a log-likelihood of -936.62. This suggests that a seasonal pattern with a 12-month cycle is the primary driver of fluctuations in Dengue incidence, with limited influence from autoregressive or moving average components. The model projected cases rising from 253 cases to 379 cases by late 2026. Temperature was a significant predictor (p\\u0026thinsp;=\\u0026thinsp;0.008), while precipitation had a weaker effect (p\\u0026thinsp;=\\u0026thinsp;0.045). The 95% confidence interval (CI) ranged from 150 to 525 cases (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor Malaria incidence, the best-fitting model was ARIMAX(1,2,0)(2,12,0). The model\\u0026rsquo;s AIC was 56,649.89, and the BIC was 56,712.91, with a log-likelihood of -28,315.95. This suggests that Malaria incidence follows a more complex seasonal pattern, with significant autoregressive and differencing components necessary to capture long-term trends and periodic variation. Incidence was forecasted to peak in wet months, reaching up to 156 cases by the end of 2026, with a 95% CI of 55 to 210 cases (Fig.\\u0026nbsp;17). Precipitation had a strong effect (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while temperature was only marginally significant (p\\u0026thinsp;=\\u0026thinsp;0.072).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe best-fitting ARIMAX(2,2,0)(0,12,0) model for West Nile Virus incidence had an AIC of 8615.65, a BIC of 8652.76, and a log-likelihood of -4299.83. The presence of two autoregressive and two differencing components suggests that WNV incidence follows a long-term increasing or decreasing trend, with seasonal variations occurring at a 12-month interval. Forecasts showed periodic outbreaks, with incidence fluctuating between 17 to 32 cases, and peak months exceeding 50 cases (Fig.\\u0026nbsp;18). Precipitation had a strong negative effect (p\\u0026thinsp;=\\u0026thinsp;0.002), while temperature was not significant (p\\u0026thinsp;=\\u0026thinsp;0.36).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study provides a comprehensive analysis of the incidence, geographic distribution, seasonality, and climatic associations of dengue, malaria, and WNV in the U.S. over multiple decades. These results, based on the fact that climate change presents significant risks to human health and heightens the likelihood of emerging diseases in the coming years[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], help understand how climatic factors influence vector-borne diseases, predict outbreaks, and implement effective control measures[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIncidence Patterns and Disease Burden\\u003c/h2\\u003e \\u003cp\\u003eWNV had the highest mean incidence and showed the greatest variability, indicating that outbreaks are sporadic yet severe, likely influenced by mosquito population dynamics, bird reservoirs, and climate fluctuations. The presence of extreme outliers in WNV incidence, as highlighted in the log-scale visualization, reinforces the idea that large-scale outbreaks occur intermittently rather than through sustained endemic transmission. Studies have found a clear association between extreme heat events and heightened outbreak intensity in human populations[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], which goes in line with our study findings. These results underscore the role of climate variability as a key driver of vector-borne disease dynamics, where temperature fluctuations may not maintain continuous transmission but instead create conditions that trigger sudden, large-scale outbreaks when critical environmental thresholds are reached[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur study findings indicate that dengue has the lowest mean incidence but experiences periodic spikes, suggesting that transmission is primarily episodic rather than sustained. According to the CDC, dengue in the U.S. mainly affects travelers returning from endemic regions, with only sporadic outbreaks of locally acquired cases[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], which aligns with our findings.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, malaria had the highest overall case count but a low median monthly incidence, reflecting its historical prevalence in the U.S. before successful eradication efforts. While sporadic cases continue to be reported, these are likely imported rather than locally transmitted. According to the CDC, locally acquired mosquito-borne malaria has not occurred in the U.S. since 2003, when a small outbreak of Plasmodium vivax malaria was identified in Florida[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Despite this, the overall risk of local transmission remains extremely low[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. However, Anopheles mosquitoes, which are present across many regions, remain capable of transmitting malaria if they bite an infected individual. The risk is highest in areas where climate conditions allow these vectors to survive year-round and where travelers from malaria-endemic regions are present[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], aligning with our study\\u0026rsquo;s findings.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGeographic Distribution of Vector-Borne Diseases in the United States\\u003c/h2\\u003e \\u003cp\\u003eThe geographic distribution of disease burden further illustrates these trends, with North Dakota (2,819.9 cases), South Dakota (3,190.4 cases), Colorado (1,756.5 cases), and Nebraska (1,466.8 cases) ranking among the highest-incidence states\\u0026mdash;primarily due to recurrent WNV outbreaks. WNV is most prevalent in these states because warm summers and drier conditions create favorable environments for Culex mosquito populations, promoting virus amplification and transmission[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. In contrast, dengue remains concentrated in southern regions, where Aedes aegypti and Aedes albopictus thrive in warm, humid environments with frequent rainfall, facilitating periodic outbreaks. This pattern aligns with Chen et al.\\u0026rsquo;s study, which identified similar climatic drivers of dengue transmission[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Meanwhile, malaria cases in the U.S. remain sporadic and primarily linked to travel-related introductions, despite the presence of Anopheles mosquito vectors in many regions[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The relatively low risk of malaria transmission is likely due to effective vector control programs, shorter mosquito lifespans in temperate climates, and limited exposure to infected individuals needed to sustain transmission cycles[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSeasonality and Long-Term Trends in Vector-Borne Disease Transmission\\u003c/h2\\u003e \\u003cp\\u003eWNV exhibited high variability in incidence but did not follow a consistent seasonal pattern. While WNV transmission peaks during warm months, its outbreak timing remains unpredictable due to several ecological and environmental factors[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Unlike dengue, which primarily relies on \\u003cem\\u003eAedes\\u003c/em\\u003e mosquitoes, WNV transmission depends on both \\u003cem\\u003eCulex\\u003c/em\\u003e mosquito vectors and bird reservoir hosts[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Variability in bird migration, and mosquito abundance can lead to fluctuating transmission patterns rather than predictable seasonal peaks[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Additionally, climatic anomalies such as droughts and extreme heat events play a critical role in WNV outbreaks[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Research has shown that higher temperatures accelerate viral replication within mosquitoes, while drought conditions may increase transmission by concentrating bird populations and reducing water sources, forcing birds and mosquitoes into closer contact[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This pattern aligns with our findings, where WNV incidence shows episodic surges rather than a strict seasonal trend.\\u003c/p\\u003e \\u003cp\\u003eDengue follows a strong seasonal pattern, peaking in late summer and early fall, driven by elevated temperatures and increased precipitation that enhance \\u003cem\\u003eAedes\\u003c/em\\u003e mosquito populations and transmission risk which aligns with prior research studies[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFor Malaria, the STL decomposition highlights a strong seasonality, with a regular pattern of increased cases in the summer months. This seasonal trend aligns with the known life cycle of Anopheles mosquitoes, which are more active and reproduce more efficiently in warm temperatures[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWNV incidence spiked in the early 2000s before declining, likely due to vector control efforts, and enhanced surveillance measures. Following its introduction in 1999, significant U.S. government investments in vector-borne disease monitoring and research led to notable short-term improvements in surveillance, contributing to better outbreak detection and response[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Dengue has steadily increased since the 1980s, driven by climate change, urbanization, and travel-related introductions. This issue is further compounded by changing environmental and ecological conditions that enhance dengue vector persistence[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], along with increased international travel contributing to case importation[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. In contrast, malaria has declined, reflecting effective vector control and minimal local transmission[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImpact of Climate Variability on Vector-Borne Disease Incidence\\u003c/h2\\u003e \\u003cp\\u003eSpearman\\u0026rsquo;s correlation analysis revealed that all three diseases\\u0026mdash;WNV, dengue, and malaria\\u0026mdash;exhibited significant negative correlations with both precipitation and temperature. For WNV, dry conditions and lower precipitation levels were associated with increased transmission, while temperature variations modulated the transmission patterns. Similarly, lower precipitation levels correlated with increased dengue incidence, likely due to the accumulation of stagnant water in urban environments that favors mosquito breeding, while extreme temperatures may have disrupted virus replication[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. In contrast to other studies that showed that dengue thrives in humid weather[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], this finding suggests that local environmental conditions and human-driven water storage practices may also play a significant role in disease transmission. While WNV benefits from dry conditions due to increased vector-host interactions[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e], dengue incidence may rise during dry periods because water storage practices create artificial breeding sites for Aedes mosquitoes[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Malaria showed a weaker but statistically significant negative correlation with both precipitation and temperature, suggesting that excessive rainfall could wash away breeding sites, reducing transmission potential, while lower temperatures might slow parasite development[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTemporal Dynamics and Lag Effects\\u003c/h2\\u003e \\u003cp\\u003eCross-correlation analysis further elucidated the temporal relationships between climate variables and disease incidence. Significant negative correlations at lags of 0 to -6 months for WNV and dengue reinforce the notion that temperature and precipitation fluctuations influence disease transmission within a six-month window. Malaria, however, did not exhibit significant lagged effects, suggesting a more complex interplay between climatic and non-climatic factors.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eNonlinear Associations and Predictive Modeling\\u003c/h2\\u003e \\u003cp\\u003eGeneralized Additive Models (GAMs) were employed to assess the nonlinear relationships between climate variables and disease incidence, revealing varying levels of association across the three vector-borne diseases studied.\\u003c/p\\u003e \\u003cp\\u003eFor WNV, precipitation emerged as a significant predictor (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while temperature did not (p\\u0026thinsp;=\\u0026thinsp;0.766). The adjusted R\\u0026sup2; value of 0.0392 suggests that precipitation accounts for a modest proportion of WNV variability. The negative association with precipitation is consistent with previous research indicating that dry conditions favor WNV transmission by reducing water availability, leading to increased bird-mosquito interactions in concentrated water sources[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The absence of a significant temperature effect may reflect the complex ecological interplay between Culex mosquitoes, avian hosts, and environmental conditions, suggesting that factors such as humidity, land use, and vector-host dynamics may play a more prominent role than temperature alone[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFor dengue, neither temperature (p\\u0026thinsp;=\\u0026thinsp;0.953) nor precipitation (p\\u0026thinsp;=\\u0026thinsp;0.212) showed significant effects, with a notably low adjusted R\\u0026sup2; of 0.0088. This suggests that climate variability alone does not sufficiently explain dengue incidence patterns in the study area. The lack of association may be due to the predominant role of other environmental and socio-ecological factors, such as urbanization, vector control measures, and human movement, which influence Aedes mosquito populations and dengue transmission. Prior studies have indicated that while temperature and precipitation play roles in dengue epidemiology, their impact is often modulated by additional determinants such as water storage practices and artificial breeding sites[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn contrast, malaria incidence exhibited a strong relationship with climate variability. Both temperature and precipitation were highly significant predictors (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with an adjusted R\\u0026sup2; of 0.0836. This finding aligns with the well-documented ecological dependencies of Anopheles mosquitoes, whose reproductive cycles and parasite development rates are directly influenced by climatic conditions[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eForecasting Trends and Implications\\u003c/h2\\u003e \\u003cp\\u003eARIMAX modeling demonstrated that climate variables contribute to the seasonal and long-term trends of disease incidence. The best-fitting ARIMAX(2,2,0)(0,12,0) model for WNV incidence suggested periodic outbreaks, with precipitation playing a significant role (p\\u0026thinsp;=\\u0026thinsp;0.002), likely due to its impact on mosquito breeding sites and bird-mosquito interactions, which influence disease transmission dynamics. This aligns with findings from Paz and Semenza, who reported that drier conditions facilitate WNV outbreaks by concentrating mosquito populations and increasing contact with avian hosts[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDengue incidence exhibited a strong seasonal component with a 12-month cycle, with temperature emerging as a significant predictor (p\\u0026thinsp;=\\u0026thinsp;0.008), consistent with findings from Messina et al., who demonstrated that dengue transmission increases with higher temperatures due to accelerated mosquito development and virus replication[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMalaria incidence exhibited a complex seasonal pattern, peaking during wet months, with precipitation exerting a dominant influence (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This is consistent with the findings of Caminade et al., who reported that increased precipitation enhances mosquito replication and malaria transmission[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. These projections emphasize the need for proactive vector control and climate adaptation strategies to mitigate disease outbreaks.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003ePublic Health Implications and Future Directions\\u003c/h2\\u003e \\u003cp\\u003eThe findings from this study underscore the need for climate-adaptive disease surveillance and vector control strategies. WNV outbreaks may intensify under future climate scenarios characterized by increased droughts and heatwaves, necessitating targeted interventions in high-risk states. Dengue\\u0026rsquo;s sporadic yet increasing presence in the U.S. warrants strengthened monitoring, particularly in regions with growing Aedes mosquito populations. Malaria remains a low-risk threat, but the presence of competent vectors in many areas highlights the importance of maintaining strong public health infrastructure to prevent local transmission.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eLimitations\\u003c/h2\\u003e \\u003cp\\u003eWhile this study provides valuable insights into the relationship between climate variability and vector-borne diseases, certain limitations must be acknowledged. First, the reliance on aggregated population-level data limits the ability to infer individual-level causality, as factors such as human movement and socioeconomic conditions were not accounted for. Second, variations in disease reporting practices across states and periods may introduce inconsistencies in data quality, potentially affecting trend interpretations. Lastly, this study does not account for the potential effects of vector control measures, or socioeconomic factors, which can influence disease transmission independently of climate variables.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study confirms that WNV exhibits the highest incidence and variability, while dengue and malaria remain lower in prevalence but follow distinct transmission patterns. The geographic distribution of disease burden highlights that WNV is concentrated in drier, high-risk states, whereas dengue is localized to southern humid regions, and malaria remains sporadic. Seasonality analysis reinforces that WNV does now follow a seasonal pattern, whereas, dengue follows a seasonal pattern peak in late summer and early fall, and malaria follows a consistent summer peak. Climate variability plays a crucial role in shaping these patterns, with drier conditions amplifying WNV outbreaks while fluctuating precipitation and temperature contribute to dengue and malaria transmission dynamics.\\u003c/p\\u003e \\u003cp\\u003eFuture research should integrate additional environmental and socio-ecological factors to refine predictive models and improve public health interventions. Enhanced surveillance, climate-informed vector control strategies, and early warning systems will be critical in adapting to the changing epidemiology of vector-borne diseases in a warming world.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate.\\u003c/strong\\u003e This study utilized publicly available data from Project Tycho and PRISM. Data is deidentified. No ethical approval was required for this study,\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u0026nbsp;\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u0026nbsp;\\u003c/strong\\u003eAvailable as part of Project Tycho and PRISM databases.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u003c/strong\\u003e The authors have nothing to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eNone.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions:\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAli Hemade:\\u003c/strong\\u003e Conceptualization, Methodology, Data Curation, Data Collection, Statistical Analysis, Formal Analysis, Writing \\u0026ndash; Original Draft (Methods and Results). Writing \\u0026ndash; Review and Editing.\\u003cbr\\u003e\\u003cstrong\\u003eMaria Akiki:\\u003c/strong\\u003e Writing \\u0026ndash; Original Draft (Introduction and Discussion).\\u003cbr\\u003e\\u003cstrong\\u003ePascale Salameh:\\u003c/strong\\u003e Supervision, Writing \\u0026ndash; Review \\u0026amp; Editing.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003ePielke Jr RA: \\u003cstrong\\u003eWhat is climate change?\\u003c/strong\\u003e \\u003cem\\u003eEnergy \\u0026amp; environment \\u003c/em\\u003e2004, \\u003cstrong\\u003e15\\u003c/strong\\u003e(3):515-520.\\u003c/li\\u003e\\n\\u003cli\\u003eField CB, Barros VR: \\u003cstrong\\u003eClimate change 2014\\u0026ndash;Impacts, adaptation and vulnerability: Regional aspects\\u003c/strong\\u003e: Cambridge University Press; 2014.\\u003c/li\\u003e\\n\\u003cli\\u003eThomson MC, Stanberry LR: \\u003cstrong\\u003eClimate change and vectorborne diseases\\u003c/strong\\u003e. \\u003cem\\u003eNew England Journal of Medicine \\u003c/em\\u003e2022, \\u003cstrong\\u003e387\\u003c/strong\\u003e(21):1969-1978.\\u003c/li\\u003e\\n\\u003cli\\u003eWaits A, Emelyanova A, Oksanen A, Abass K, Rautio A: \\u003cstrong\\u003eHuman infectious diseases and the changing climate in the Arctic\\u003c/strong\\u003e. \\u003cem\\u003eEnvironment International \\u003c/em\\u003e2018, \\u003cstrong\\u003e121\\u003c/strong\\u003e:703-713.\\u003c/li\\u003e\\n\\u003cli\\u003eWilson ML: \\u003cstrong\\u003eEcology and infectious disease\\u003c/strong\\u003e. \\u003cem\\u003eEcosystem change and public health: a global perspective \\u003c/em\\u003e2001, \\u003cstrong\\u003e283\\u003c/strong\\u003e:324.\\u003c/li\\u003e\\n\\u003cli\\u003eLeibovici DG, Bylund H, Bj\\u0026ouml;rkman C, Tokarevich N, Thierfelder T, Eveng\\u0026aring;rd B, Quegan S: \\u003cstrong\\u003eAssociating land cover changes with patterns of incidences of climate-sensitive infections: an example on tick-borne diseases in the Nordic area\\u003c/strong\\u003e. \\u003cem\\u003eInternational journal of environmental research and public health \\u003c/em\\u003e2021, \\u003cstrong\\u003e18\\u003c/strong\\u003e(20):10963.\\u003c/li\\u003e\\n\\u003cli\\u003eGao H, Wang L, Ma J, Gao X, Xiao J, Wang H: \\u003cstrong\\u003eModeling the current distribution suitability and future dynamics of Culicoides imicola under climate change scenarios\\u003c/strong\\u003e. \\u003cem\\u003ePeerJ \\u003c/em\\u003e2021, \\u003cstrong\\u003e9\\u003c/strong\\u003e:e12308.\\u003c/li\\u003e\\n\\u003cli\\u003eMojahed N, Mohammadkhani MA, Mohamadkhani A: \\u003cstrong\\u003eClimate crises and developing vector-borne diseases: a narrative review\\u003c/strong\\u003e. \\u003cem\\u003eIranian journal of public health \\u003c/em\\u003e2022, \\u003cstrong\\u003e51\\u003c/strong\\u003e(12):2664.\\u003c/li\\u003e\\n\\u003cli\\u003eGubler DJ: \\u003cstrong\\u003e1 Vector-Borne Disease Emergence and Resurgence\\u003c/strong\\u003e. 2008.\\u003c/li\\u003e\\n\\u003cli\\u003eBansal V, Munjal J, Lakhanpal S, Gupta V, Garg A, Munjal RS, Jain R: \\u003cstrong\\u003eEpidemiological shifts: The emergence of malaria in America\\u003c/strong\\u003e. 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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.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe 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\\u0026rsquo;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.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eOur 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.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThis 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.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Climate Variability and Vector-Borne Disease Dynamics: A Time-Series Analysis of Dengue, Malaria, and West Nile Virus in the United States\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-03-19 05:30:14\",\"doi\":\"10.21203/rs.3.rs-6208975/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d77a0ba5-2f67-464b-b205-d3642a9eaaf7\",\"owner\":[],\"postedDate\":\"March 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-24T18:53:27+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-03-19 05:30:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6208975\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6208975\",\"identity\":\"rs-6208975\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}