Vector-borne disease incidence and burden are on the rise. Weather events and climate patterns are known to influence vector populations and disease distribution and incidence. Changes in weather trends and climatic factors can shift seasonal vector activity and host behavior, thus altering pathogen distribution and introducing diseases to new geographic regions. With the upward trend in global temperature, changes in the incidence and distribution of disease vectors possibly linked to climate change have been documented. Forecasting and modeling efforts are valuable for incorporating climate into predicting changes in vector and vector-borne disease distribution. These predictions serve to optimize disease outbreak preparedness and response. The purpose of this scoping review was to describe the use of climate data in vector-borne disease prediction in North America between 2000 and 2022. The most investigated diseases were West Nile virus infection, Lyme disease, and dengue. The uneven geographical distribution of publications could suggest regional differences in the availability of surveillance data required for vector-borne disease predictions and forecasts across the United States, Canada, and Mexico. Studies incorporated environmental data from ground-based sources, satellite data, previously existing data, and field-collected data. While environmental data such as meteorological and topographic factors were well-represented, further research is warranted to ascertain if relationships with less common variables, such as oceanographic characteristics and drought, hold among various vector populations and throughout wider geographical areas. This review provides a catalogue of recently used climatic data that can inform future assessments of the value of such data in vector-borne disease models.

The majority of vector-borne disease cases reported annually in the United States are caused by pathogens spread by the blacklegged tick, Ixodes scapularis. The number and geographic distribution of cases have increased as the geographic range and abundance of the tick have expanded in recent decades. A large proportion of Lyme disease and other I. scapularis-borne diseases are associated with nymphal tick bites; likelihood of such bites generally increases with increasing nymphal densities. National tick surveillance was initiated in 2018 to track changes in the distribution and abundance of medically important ticks at the county spatial scale throughout the United States. Tick surveillance records, including historical data collected prior to the initiation of the national program, are collated in the ArboNET Tick Module database. Through exploration of ArboNET Tick Module data, we found that efforts to quantify the density of host-seeking I. scapularis nymphs (DON) were unevenly distributed among geographic regions with the greatest proportion of counties sampled in the Northeast and Upper Midwest. Submissions covering tick collections from 2004 through 2022 revealed extensive variation in DON estimates at collection site, county, state, and regional spatial scales. Throughout the entire study period, county DON estimates ranged from 0.0 to 488.5 nymphs/1,000 m(2). Although substantial variation was recorded within regions, DON estimates were greatest in the Northeast, Upper Midwest, and northern states within the Southeast regions (Virginia and North Carolina); densities were intermediate in the Ohio Valley and very low in the South and Northern Rockies and Plains regions. The proportion of counties classified as moderate or high DON was greater in the Northeast, Ohio Valley, and Southeast regions during the 2004 through 2017 time period (prior to initiation of the national tick surveillance program) compared to 2018 through 2022; DON estimates remained similarly low between these time periods in the South and the Northern Rockies and Plains regions. Despite the limitations described herein, the ArboNET Tick Module provides useful data for tracking changes in acarological risk across multiple geographic scales and long periods of time.

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

The majority of vector-borne disease cases reported in the United States (U.S.) are caused by pathogens spread by the blacklegged tick, Ixodes scapularis. In recent decades, the geographic ranges of the tick and its associated human pathogens have expanded, putting an increasing number of communities at risk for tick-borne infections. In 2018, the U.S. Centers for Disease Control and Prevention (CDC) initiated a national tick surveillance program to monitor changes in the distribution and abundance of ticks and the presence and prevalence of human pathogens in them. We assessed the geographical representativeness of prevalence data submitted to CDC as part of the national tick surveillance effort. We describe county, state, and regional variation in the prevalence of five human pathogens (Borrelia burgdorferi sensu stricto (s.s.), Borrelia mayonii, Borrelia miyamotoi, Anaplasma phagocytophilum, and Babesia microti) in host-seeking I. scapularis and I. pacificus nymphs and adults. Although I. scapularis and I. pacificus are widely distributed in the eastern and western U.S., respectively, pathogen prevalence was estimated predominantly in ticks collected in the Northeast, Ohio Valley, and Upper Midwest regions, where human Lyme disease cases are most commonly reported. Within these regions, we found that state and regional estimates of pathogen prevalence generally reached predictable and stable levels, but variation in prevalence estimates at the sub-state level was considerable. Borrelia burgdorferi s.s. was the most prevalent and widespread pathogen detected. Borrelia miyamotoi and A. phagocytophilum shared a similarly broad geographic range, but were consistently detected at much lower prevalence compared with B. burgdorferi s.s. Babesia microti was detected at similar prevalence to A. phagocytophilum, where both pathogens co-occurred, but was reported over a much more limited geographic range compared with A. phagocytophilum or B. burgdorferi s.s. Borrelia mayonii was identified at very low prevalence with a focal distribution within the Upper Midwest. National assessments of risk for tick-borne diseases need to be improved through collection and testing of ticks in currently under-represented regions, including the West, South, Southeast, and eastern Plains states.

Tick-borne diseases continue to threaten human health across the United States. Both active and passive tick surveillance can complement human case surveillance, providing spatio-temporal information on when and where humans are at risk for encounters with ticks and tick-borne pathogens. However, little work has been done to assess the concordance of the acarological risk metrics from each surveillance method. We used data on Ixodes scapularis and its associated human pathogens from Connecticut (2019-2021) collected through active collections (drag sampling) or passive submissions from the public to compare county estimates of tick and pathogen presence, infection prevalence, and tick abundance by life stage. Between the surveillance strategies, we found complete agreement in estimates of tick and pathogen presence, high concordance in infection prevalence estimates for Anaplasma phagocytophilum, Borrelia burgdorferi sensu stricto, and Babesia microti, but no consistent relationships between actively and passively derived estimates of tick abundance or abundance of infected ticks by life stage. We also compared nymphal metrics (i.e., pathogen prevalence in nymphs, nymphal abundance, and abundance of infected nymphs) with reported incidence of Lyme disease, anaplasmosis, and babesiosis, but did not find any consistent relationships with any of these metrics. The small spatial and temporal scale for which we had consistently collected active and passive data limited our ability to find significant relationships. Findings are likely to differ if examined across a broader spatial or temporal coverage with greater variation in acarological and epidemiological outcomes. Our results indicate similar outcomes between some actively and passively derived tick surveillance metrics (tick and pathogen presence, pathogen prevalence), but comparisons were variable for abundance estimates.

Ivermectin (IVM)-treated birds provide the potential for targeted control of Culex mosquitoes to reduce West Nile virus (WNV) transmission. Ingestion of IVM increases mosquito mortality, which could reduce WNV transmission from birds to humans and in enzootic maintenance cycles affecting predominantly bird-feeding mosquitoes and from birds to humans. This strategy might also provide an alternative method for WNV control that is less hampered by insecticide resistance and the logistics of large-scale pesticide applications. Through a combination of field studies and modeling, we assessed the feasibility and impact of deploying IVM-treated birdfeed in residential neighborhoods to reduce WNV transmission. We first tracked 105 birds using radio telemetry and radio frequency identification to monitor their feeder usage and locations of nocturnal roosts in relation to five feeder sites in a neighborhood in Fort Collins, Colorado. Using these results, we then modified a compartmental model of WNV transmission to account for the impact of IVM on mosquito mortality and spatial movement of birds and mosquitoes on the neighborhood level. We found that, while the number of treated lots in a neighborhood strongly influenced the total transmission potential, the arrangement of treated lots in a neighborhood had little effect. Increasing the proportion of treated birds, regardless of the WNV competency status, resulted in a larger reduction in infection dynamics than only treating competent birds. Taken together, model results indicate that deployment of IVM-treated feeders could reduce local transmission throughout the WNV season, including reducing the enzootic transmission prior to the onset of human infections, with high spatial coverage and rates of IVM-induced mortality in mosquitoes. To improve predictions, more work is needed to refine estimates of daily mosquito movement in urban areas and rates of IVM-induced mortality. Our results can guide future field trials of this control strategy.

Every year vector-borne diseases (VBDs) result in significant illness and death in the United States. Between 2004 and 2019, >800,000 cases of VBDs were reported to the U.S. Centers for Disease Control and Prevention, with the number of reported disease cases more than doubling over this period.1 Furthermore, in the summer of 2021, the largest local outbreak of West Nile virus (WNV) disease on record occurred in Arizona.2 This event, which was largely obscured by the COVID-19 pandemic, was likely influenced by a wetter-than-average monsoonal season.3

BACKGROUND: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. METHODS: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. RESULTS: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. CONCLUSIONS: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).