Understanding transmission risk and predicting environmental suitability for Mayaro Virus in Central and South America.

Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4-100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6-68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies.

A geopositioned and evidence-graded pan-species compendium of Mayaro virus occurrence.

Mayaro Virus (MAYV) is an emerging health threat in the Americas that can cause febrile illness as well as debilitating arthralgia or arthritis. To better understand the geographic distribution of MAYV risk, we developed a georeferenced database of MAYV occurrence based on peer-reviewed literature and unpublished reports. Here we present this compendium, which includes both point and polygon locations linked to occurrence data documented from its discovery in 1954 until 2022. We describe all methods used to develop the database including data collection, georeferencing, management and quality-control. We also describe a customized grading system used to assess the quality of each study included in our review. The result is a comprehensive, evidence-graded database of confirmed MAYV occurrence in humans, non-human animals, and arthropods to-date, containing 262 geo-positioned occurrences in total. This database - which can be updated over time - may be useful for local spill-over risk assessment, epidemiological modelling to understand key transmission dynamics and drivers of MAYV spread, as well as identification of major surveillance gaps.

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.

Ensemble ecological niche modeling of West Nile virus probability in Florida.

Ecological Niche Modeling is a process by which spatiotemporal, climatic, and environmental data are analyzed to predict the distribution of an organism. Using this process, an ensemble ecological niche model for West Nile virus habitat prediction in the state of Florida was developed. This model was created through the weighted averaging of three separate machine learning models-boosted regression tree, random forest, and maximum entropy-developed for this study using sentinel chicken surveillance and remote sensing data. Variable importance differed among the models. The highest variable permutation value included mean dewpoint temperature for the boosted regression tree model, mean temperature for the random forest model, and wetlands focal statistics for the maximum entropy mode. Model validation resulted in area under the receiver curve predictive values ranging from good [0.8728 (95% CI 0.8422-0.8986)] for the maximum entropy model to excellent [0.9996 (95% CI 0.9988-1.0000)] for random forest model, with the ensemble model predictive value also in the excellent range [0.9939 (95% CI 0.9800-0.9979]. This model should allow mosquito control districts to optimize West Nile virus surveillance, improving detection and allowing for a faster, targeted response to reduce West Nile virus transmission potential.

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

Eastern equine encephalitis virus (EEEV), an Alphavirus from family Togaviridae, is a highly pathogenic arbovirus affecting the eastern United States, especially Florida. Effects of the Southern Oscillation Index (SOI), precipitation, and cooling degree days on EEEV horse case data in Florida from 2004 to 2018 were modeled using distributed lag nonlinear models (DLNMs). The analysis was conducted at statewide and regional scales. DLNMs were used to model potential delayed effects of the covariates on monthly counts of horse cases. Both models confirmed a seasonal trend in EEEV transmission and found that precipitation, cooling degree days, and the SOI were all predictors of monthly numbers of horse cases. EEEV activity in horses was associated with higher amounts of rainfall during the month of transmission at the statewide scale, as well as the prior 3 mo at the regional scale, fewer cooling degree days during the month of transmission and the preceding 3 mo and high SOI values during the month and the previous 2 mo, and SOI values in the prior 2 to 8 mo. Horse cases were lower during El Niño winters but higher during the following summer, while La Niña winters were associated with higher numbers of cases and fewer during the following summer. At the regional scale, extremely low levels of precipitation were associated with a suppression of EEEV cases for 3 mo. Given the periodicity and potential predictability of El Niño Southern Oscillation (ENSO) cycles, precipitation, and temperature, these results may provide a method for predicting EEEV risk potential in Florida.