West Nile virus is a widely spread arthropod-born virus, which has mosquitoes as vectors and birds as reservoirs. Humans, as dead-end hosts of the virus, may suffer West Nile Fever (WNF), which sometimes leads to death. In Europe, the first large-scale epidemic of WNF occurred in 1996 in Romania. Since then, human cases have increased in the continent, where the highest number of cases occurred in 2018. Using the location of WNF cases in 2017 and favorability models, we developed two risk models, one environmental and the other spatio-environmental, and tested their capacity to predict in 2018: 1) the location of WNF; 2) the intensity of the outbreaks (i.e. the number of confirmed human cases); and 3) the imminence of the cases (i.e. the Julian week in which the first case occurred). We found that climatic variables (the maximum temperature of the warmest month and the annual temperature range), human-related variables (rain-fed agriculture, the density of poultry and horses), and topo-hydrographic variables (the presence of rivers and altitude) were the best environmental predictors of WNF outbreaks in Europe. The spatio-environmental model was the most useful in predicting the location of WNF outbreaks, which suggests that a spatial structure, probably related to bird migration routes, has a role in the geographical pattern of WNF in Europe. Both the intensity of cases and their imminence were best predicted using the environmental model, suggesting that these features of the disease are linked to the environmental characteristics of the areas. We highlight the relevance of river basins in the propagation dynamics of the disease, as outbreaks started in the lower parts of the river basins, from where WNF spread towards the upper parts. Therefore, river basins should be considered as operational geographic units for the public health management of the disease.

Ecuador has both high richness and high endemism, which are increasingly threatened by anthropic pressures, including roads. Research evaluating the effects of roads remains scarce, making it difficult to develop mitigation plans. Here, we present the first national assessment of wildlife mortality on roads that allow us to (1) estimate roadkill rates per species, (2) identify affected species and areas, and (3) reveal knowledge gaps. We bring together data from systematic surveys and citizen science efforts to present a dataset with 5010 wildlife roadkill records from 392 species, and we also provide 333 standardized corrected roadkill rates calculated on 242 species. Systematic surveys were reported by ten studies from five Ecuadorian provinces, revealing 242 species with corrected roadkill rates ranging from 0.03 to 171.72 ind./km/year. The highest rates were for the yellow warbler Setophaga petechia in Galapagos (171.72 ind./km/year), the cane toad Rhinella marina in Manabi (110.70 ind./km/year), and the Galapagos lava lizard Microlophus albemarlensis (47.17 ind./km/year). Citizen science and other nonsystematic monitoring provided 1705 roadkill records representing all 24 provinces in Ecuador and 262 identified species. The common opossum Didelphis marsupialis, the Andean white‐eared opossum Didelphis pernigra, and the yellow warbler Setophaga petechia were more commonly reported (250, 104, and 81 individuals, respectively). Across all sources, we found 15 species listed as “Threatened” and six as “Data Deficient” by the IUCN. We recommend stronger research efforts in areas where the mortality of endemic or threatened species could be critical for populations, such as in Galapagos. This first country‐wide assessment of wildlife mortality on Ecuadorian roads represents contributions from academia, members of the public, and government, underlining the value of wider engagement and collaboration. We hope these findings and the compiled dataset will guide sensible driving and sustainable planning of infrastructure in Ecuador and, ultimately, contribute to reduce wildlife mortality on roads.

Risk for West Nile Virus Transmission, Africa

West Nile virus (WNV) is a globally significant vector-borne disease that is primarily transmitted between birds and mosquitoes. Recently, there has been an increase in WNV in southern Europe, with new cases reported in more northern regions. Bird migration plays a crucial role in the introduction of WNV in distant areas. To better understand and address this complex issue, we adopted a One Health approach, integrating clinical, zoological, and ecological data. We analyzed the role of migratory birds in the Palaearctic-African region in the spread of WNV across Africa and Europe. We categorized bird species into breeding and wintering chorotypes based on their distribution during the breeding season in the Western Palaearctic and the wintering season in the Afrotropical region, respectively. By linking these chorotypes to the occurrence of WNV outbreaks in both continents throughout the annual bird migration cycle, we investigated the relationship between migratory patterns and virus spread. We demonstrate that WNV-risk areas are interconnected through the migration of birds. We identified a total of 61 species that potentially contribute to the intercontinental spread of the virus or its variants, as well as pinpointed high-risk areas for future outbreaks. This interdisciplinary approach, which considers the interconnectedness of animals, humans, and ecosystems, represents a pioneering effort to establish connections between zoonotic diseases across continents. The findings of our study can aid in anticipating the arrival of new WNV strains and predicting the occurrence of other re-emerging diseases. By incorporating various disciplines, we can enhance our understanding of these complex dynamics and provide valuable insights for proactive and comprehensive disease management strategies.

Background: West Nile virus (WNV) is a flavivirus with an enzootic cycle between birds and mosquitoes; humans and horses are incidental dead-end hosts. In 2020, the largest outbreak of West Nile virus infection in the Iberian Peninsula occurred, with 141 clusters in horses and 77 human cases.Aim: We analysed which drivers influence spillover from the cycle to humans and equines and identified areas at risk for WNV transmission.Methods: Based on data on WNV cases in horses and humans in 2020 in Portugal and Spain, we developed logistic regression models using environmental and anthropic variables to highlight risk areas. Models were adapted to a high-resolution risk map.Results: Cases of WNV in horses could be used as indicators of viral activity and thus predict cases in humans. The risk map of horses was able to define high-risk areas for previous cases in humans and equines in Portugal and Spain, as well as predict human and horse cases in the transmission seasons of 2021 and 2022. We found that the spatial patterns of the favourable areas for outbreaks correspond to the main hydrographic basins of the Iberian Peninsula, jointly affecting Portugal and Spain.Conclusion: A risk map highlighting the risk areas for potential future cases could be cost-effective as a means of promoting preventive measures to decrease incidence of WNV infection in Europe, based on a One Health surveillance approach.

International travellers are exposed to pathogens not commonly found in their countries of residence, including West Nile virus (WNV). Due to the difficulty of its diagnosis, little is known about its distribution in Africa. Understanding the geographic extent of risk of WNV infections is a necessity for both travellers and clinicians who advise and treat them, since there is no human vaccine. To date, there is no risk map for WNV infections in humans in Africa. Having a high-resolution risk map for the virus could be of relevance before the trip, to take preventive measures, and after the trip, for appropriate diagnosis of the disease. Virus detection in humans along the African continent were collected from official reports, and published scientific research for the period 1940 to 2020, and then geo-referenced in order to use biogeographical modelling for WNV. Models were based on fuzzy logic and machine learning algorithms and were designed to identify the environmental drivers that explain the distribution of human cases and to locate favourable areas for infections. We elaborated a high-resolution risk map for WNV infections that highlights favourable areas for infections in Africa. Although WNV infections are widely spread across Africa, the risk of the disease is not homogenously distributed. Popular tourist destinations such as Morocco, Tunisia, and South Africa, are high-risk areas for WNV infection.