Rethinking vector-borne disease prediction: toward an integrative modeling approach including vector and host data

Vector-borne diseases significantly impact human and animal health, as well as global economies. Over the past decades, modeling approaches have significantly advanced our ability to understand and predict their dynamics. However, most predictive frameworks still rely predominantly on the abiotic components. This narrow focus fails to capture the ecological reality of transmission, since abiotic features alone do not ensure pathogen maintenance in the absence of appropriate hosts or vectors. The scarcity of models incorporating host-related variables underscores limitations that can over- or underestimate risk. Biotic interactions among pathogens, vectors, and hosts add layers of complexity that determine where and when transmission might occur. Integrating vector and host distributions using species-level data, supra-specific taxonomic proxies, or chorotype-based analyses offers a promising path toward more ecologically realistic predictions. Future progress will depend on improved biotic datasets, interdisciplinary collaboration, and the development of integrative frameworks that capture vector-borne disease dynamics.