Wildlife susceptibility to infectious diseases at global scales.
Proc Natl Acad Sci U S A
; 119(35): e2122851119, 2022 08 30.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2001002
ABSTRACT
Disease transmission prediction across wildlife is crucial for risk assessment of emerging infectious diseases. Susceptibility of host species to pathogens is influenced by the geographic, environmental, and phylogenetic context of the specific system under study. We used machine learning to analyze how such variables influence pathogen incidence for multihost pathogen assemblages, including one of direct transmission (coronaviruses and bats) and two vector-borne systems (West Nile Virus [WNV] and birds, and malaria and birds). Here we show that this methodology is able to provide reliable global spatial susceptibility predictions for the studied host-pathogen systems, even when using a small amount of incidence information (i.e., [Formula see text] of information in a database). We found that avian malaria was mostly affected by environmental factors and by an interaction between phylogeny and geography, and WNV susceptibility was mostly influenced by phylogeny and by the interaction between geographic and environmental distances, whereas coronavirus susceptibility was mostly affected by geography. This approach will help to direct surveillance and field efforts providing cost-effective decisions on where to invest limited resources.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Enfermedades Transmisibles Emergentes
/
Susceptibilidad a Enfermedades
/
Animales Salvajes
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Animales
Idioma:
Inglés
Revista:
Proc Natl Acad Sci U S A
Año:
2022
Tipo del documento:
Artículo
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