Wildlife susceptibility to infectious diseases at global scales.

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

SUBMITTER: Robles-Fernandez AL 

PROVIDER: S-EPMC9436312 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature