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PLoS neglected tropical diseases · 2020

Using host traits to predict reservoir host species of rabies virus.

СтатьяПоддержка исследования: вне правительства СШАПоддержка правительства СШАНазвание и аннотация: Перевод в очереди
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Worsley-Tonks Katherine E L, Escobar Luis E, Biek Roman, Castaneda-Guzman Mariana, Craft Meggan E, Streicker Daniel G, White Lauren A, Fountain-Jones Nicholas M

Аннотация

Wildlife are important reservoirs for many pathogens, yet the role that different species play in pathogen maintenance frequently remains unknown. This is the case for rabies, a viral disease of mammals. While Carnivora (carnivores) and Chiroptera (bats) are the canonical mammalian orders known to be responsible for the maintenance and onward transmission of rabies Lyssavirus (RABV), the role of most species within these orders remains unknown and is continually changing as a result of contemporary host shifting. We combined a trait-based analytical approach with gradient boosting machine learning models to identify physiological and ecological host features associated with being a reservoir for RABV. We then used a cooperative game theory approach to determine species-specific traits associated with known RABV reservoirs. Being a carnivore reservoir for RABV was associated with phylogenetic similarity to known RABV reservoirs, along with other traits such as having larger litters and earlier sexual maturity. For bats, location in the Americas and geographic range were the most important predictors of RABV reservoir status, along with having a large litter. Our models identified 44 carnivore and 34 bat species that are currently not recognized as RABV reservoirs, but that have trait profiles suggesting their capacity to be or become reservoirs. Further, our findings suggest that potential reservoir species among bats and carnivores occur both within and outside of areas with current RABV circulation. These results show the ability of a trait-based approach to detect potential reservoirs of infection and could inform rabies control programs and surveillance efforts by identifying the types of species and traits that facilitate RABV maintenance and transmission.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты