Impact of the Southern Oscillation Index, Temperature, and Precipitation on Eastern Equine Encephalitis Virus Activity in Florida

Climate-induced expansion of Lyme disease in east central Ohio

The geographical distribution of Lyme disease has been attributed to changes in Earth's climate and associated distribution of its vector, ticks of the genus Ixodes. This study focuses on the impact of climatic and meteorological conditions on Lyme disease transmission in East Central Ohio, an emerging hotspot of cases. Using county-level data from 2001 to 2023, we analyzed the relationship between Lyme disease cases and temperature, precipitation, and the Southern Oscillation Index (SOI) using a distributed lag nonlinear model (DLNM). Results show that warmer winter temperatures, higher precipitation, and negative SOI values (El Niño conditions) were significantly associated with increased Lyme disease incidence and displayed delayed effects of 6 to18 months. These findings suggest that climate change, with its potential to bring milder winters and increased spring and summer rainfall, may further exacerbate Lyme disease cases in Ohio.

The use of environmental data in descriptive and predictive models of vector-borne disease in North America

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.

Spatiotemporal Environmental Drivers of Eastern Equine Encephalitis Virus in Central Florida: Towards a Predictive Model for a Lethal Disease

Eastern equine encephalitis virus (EEEV) is a mosquito-borne virus that affects humans and horses, with a high case fatality rate in both species. The virus can be transmitted by several mosquito species and maintained in multiple reservoir hosts, making EEEV dynamics difficult to anticipate. In this study, we identified spatial and temporal factors that precede EEEV detection using sentinel chicken surveillance data from Orange County, Florida, from 2003 to 2017. We first examined the land cover and mosquito species composition associated with sentinel chicken sites. We then fit distributed lag nonlinear models of EEEV detection at the county scale, using monthly temperature, precipitation, and Southern Oscillation Index values, and at the sentinel flock-scale, using remotely sensed temperature and wetness indicators. We found positive associations between the percent wooded wetlands and the count of EEEV detections. We found Culiseta melanura (Diptera: Culicidae) were more abundant at positive sites in winter and summer, but Coquillettidia perturbans (Walker) were more abundant at positive sites in spring. In the county-wide model, precipitation, temperature, and Southern Oscillation Index values at lags of two, nine, and twelve months were significant, respectively, while temperature and wetness were significant at lags of eight and six months in the flock-specific models.

Spatiotemporal Modeling of Zoonotic Arbovirus Transmission in Northeastern Florida Using Sentinel Chicken Surveillance and Earth Observation Data

The irregular timing and spatial variation in the zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting spillover occurrence over time and across broader geographic areas, compromising effective prevention and control strategies. The objective of this study was to quantify the effects of the landscape composition and configuration and dynamic weather events on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida. We used a modeling framework that explicitly accounts for joint spatial and temporal effects and incorporates key EO (Earth Observation) information on the climate and landscape in order to more accurately quantify the environmental effects on the transmission to sentinel chickens. We investigated the environmental effects using Bernoulli generalized linear mixed effects models (GLMMs), including a site-level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. The models were executed using an integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. The GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and also performed better than non-spatial models. The results indicated a strong spatiotemporal structure in the seroconversion for both viruses, but EEEV exhibited a more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited a more gradual and diffuse structure across the study area toward the end of the sampling season. The percentage of cypress–tupelo wetland land cover within 3500 m of coop sites and the edge density of the forest land cover within 500 m had a strong positive effect on the EEEV seroconversion, while the best fitting model for WNV was the intercept-only model with spatiotemporal random effects. The lagged climatic variables included in our study did not have a strong effect on the seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid type I errors. The predictive accuracy for out-of-sample data for the EEEV seroconversion demonstrates the potential to develop a framework that incorporates temporal dynamics in order to better predict arbovirus transmission.

West Nile Virus and Eastern Equine Encephalitis Virus High Probability Habitat Identification for the Selection of Sentinel Chicken Surveillance Sites in Florida

To mitigate the effects of West Nile virus (WNV) and eastern equine encephalitis virus (EEEV), the state of Florida conducts a serosurveillance program that uses sentinel chickens operated by mosquito control programs at numerous locations throughout the state. Coop locations were initially established to detect St. Louis encephalitis virus (SLEV), and coop placement was determined based on the location of human SLEV infections that occurred between 1959 and 1977. Since the introduction of WNV into Florida in 2001, WNV has surpassed SLEV as the primary arbovirus in Florida. Identifying high probability locations for WNV and EEEV transmission and relocating coops to areas of higher arbovirus activity would improve the sensitivity of the sentinel chicken surveillance program. Using 2 existing models, this study conducted an overlay analysis to identify areas with high probability habitats for both WNV and EEEV activity. This analysis identified approximately 7,800 km2 (about 4.5% of the state) as high probability habitat for supporting both WNV and EEEV transmission. Mosquito control programs can use the map resulting from this analysis to improve their sentinel chicken surveillance programs, increase the probability of virus detection, reduce operational costs, and allow for a faster, targeted response to virus detection.

Field Analysis of Biological Factors Associated With Sites at High and Low to Moderate Risk for Eastern Equine Encephalitis Virus Winter Activity in Florida

Eastern equine encephalitis virus (EEEV) is the most pathogenic arbovirus endemic to the United States. Studies have demonstrated Florida's role as a regional reservoir for the virus and its ability to support year-round transmission. Previous research has developed risk index models for mapping locations most at risk for EEEV transmission. We compared vector abundance, vector feeding behavior, potential host species, and fauna presence at high versus low-moderate risk sites during the winter and spring. Predicted high-risk sites had a significantly greater abundance of mosquitoes overall, including Culiseta melanura (Coquillett) (Diptera: Culicidae), the primary enzootic vector of EEEV. Twenty host species were identified from Cs. melanura bloodmeals, with the majority taken from avian species. Culiseta melanura largely fed upon the Northern Cardinal (Cardinalis cardinalis (Passeriformes: Cardinalidae)), which accounted for 20-24.4% of the bloodmeals obtained from this species in years 1 and 2, respectively. One EEEV-positive mosquito pool (Cs. melanura) and nine EEEV seropositive sentinel chickens were confirmed during winter-spring collections from high-risk sites; no seropositive chickens nor mosquito pools were found at the low-moderate risk sites. These results suggest that high-risk sites for EEEV activity are characterized by habitats that support populations of Cs. melanura and which may also provide ample opportunities to feed upon Northern Cardinals. The overall low level of mosquito populations during the winter also suggests that control of Cs. melanura populations in winter at high-risk sites may prove effective in reducing EEEV transmission during the peak summer season.