# MaxEnt modeling and risk evaluation of chagas disease vectors in the domestic cycle of Hidalgo, Mexico

## TL;DR

This study uses MaxEnt modeling to map the distribution of Chagas disease vectors in Hidalgo, Mexico, to improve disease surveillance and control.

## Contribution

The study experimentally validates MaxEnt models for Chagas disease vectors using field data, a novel approach in this context.

## Key findings

- MaxEnt models showed high accuracy (AUC ≥ 0.8) in predicting vector distributions.
- Climate types, altitude, and anthropogenic factors were key determinants of vector habitats.
- Field validation confirmed model predictions and documented the presence of three vector species.

## Abstract

This study developed MaxEnt models to determine the potential distribution of four triatomine vector species of Chagas disease in the domestic cycle in Hidalgo state, Mexico: Triatoma dimidiata (Latreille, 1811), T. mexicana (Herrich-Schaeffeer, 1848), T. gerstaeckeri (Stål, 1859), and T. barberi (Usinger, 1939). We analyzed over 500 occurrence records alongside selected bioclimatic, topographic, and land cover variables. Key determinants influencing each species’ distribution included climate types, altitude, and anthropogenic factors. Model validation used statistical methods with Area Under the Curve (AUC) metrics, where AUCs ≥ 0.8 indicated good performance, along with experimental validation performed for the first time in the context of Chagas disease through targeted field collections at predicted sites. The results showed high concordance between model classifications and field data, confirming the models’ validity. The identified suitable habitat areas correlated with known ranges of the vector species, providing insights into Chagas disease transmission risk in the domestic cycle. This integrated approach not only validated the presence and absence of the modeled species but also documented the current presence of three vector species, enhancing our understanding of factors influencing vector distributions. Ultimately, this research aims to inform epidemiological control efforts and improve Chagas disease surveillance strategies.

This study used MaxEnt modeling to develop potential distribution models for the four main vectors of Chagas disease in the domestic cycle of Hidalgo, Mexico: T. dimidiata, T. mexicana, T. gerstaeckeri, and T. barberi. We analyzed over 500 occurrence records alongside environmental variables. The models were validated statistically using AUC metrics (AUCs ≥ 0.8) and experimentally through field collections at selected sites. Validation showed high concordance between model predictions and field data. Climate types were the primary contributors to most models, with precipitation and altitude also influencing some species. This integrated approach enhanced our understanding of vector habitats within environmental gradients in Hidalgo, providing valuable insights for Chagas disease surveillance and control efforts.