Improving distribution models of sparsely documented disease vectors by incorporating information on related species via joint modeling.

ABSTRACT: A necessary component of understanding vector-borne disease risk is accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propos

SUBMITTER: Mowry S 

PROVIDER: S-EPMC11925501 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature