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

Area of Science:

• Ecology
• Epidemiology
• Disease Modeling

Background:

• Vector-borne diseases pose substantial risks to human and animal health and global economies.
• Current predictive models for these diseases primarily focus on abiotic factors, neglecting crucial ecological interactions.
• This limited scope can lead to inaccurate risk assessments.

Purpose of the Study:

• To highlight the limitations of current vector-borne disease models that overlook biotic interactions.
• To advocate for the integration of host and vector data into predictive frameworks.
• To propose a more ecologically realistic approach for understanding disease dynamics.

Main Methods:

• Review of existing modeling approaches for vector-borne diseases.
• Analysis of the impact of abiotic versus biotic factors on disease transmission.
• Exploration of methods for integrating species distribution data (species-level, taxonomic proxies, chorotypes).

Main Results:

• Abiotic factors alone are insufficient for predicting pathogen maintenance and transmission.
• Models lacking host-related variables may overestimate or underestimate disease risk.
• Integrating vector and host distributions offers a more accurate predictive pathway.

Conclusions:

• Future vector-borne disease models must incorporate biotic interactions for ecological realism.
• Improved biotic datasets and interdisciplinary collaboration are essential for advancing predictive frameworks.
• Integrative approaches are key to accurately capturing vector-borne disease dynamics.