Drivers for wildlife infection are multiple and complex, particularly for vector-borne diseases. Here, we studied the role of host competence, geographic area provenance, and diversity of vector-host interactions as drivers of wild mammal infection risk to Trypanosoma cruzi, the aetiological agent of Chagas disease. We performed a systematic sampling of wild mammals in 11 states of Mexico, from 2017 to 2018. We tested the positivity of T. cruzi with the Tc24 marker in tissues samples for 61 wild mammal species (524 specimens sampled). 26 mammal species were positive for T. cruzi, of which 11 are new hosts recorded in Mexico 75 specimens were positive and 449 were negative for T. cruzi infection, yielding an overall prevalence of 14.3%. The standardized infection risk of T. cruzi of our examined specimens was similar, no matter the host species or their geographic origins. Additionally, we used published data of mammal positives for T. cruzi to complement records of T. cruzi infection in wild mammals and inferred a trophic network of Triatoma spp. (vectors) and wild mammal species in Mexico, using spatial data-mining modelling. Infection with T. cruzi was not homogeneously distributed in the inferred trophic network. This information allowed us to develop a predictive model for T. cruzi infection risk for wild mammals in Mexico, considering risk as a function of the diversity of vector-host spatial associations in a large-scale geographic context, finding that the addition of competent vectors to a multi-host parasite system amplifies host infection risk. The diversity of vector-host interactions per se constitutes a relevant driver of infection risk because hosts and vectors are not isolated from each other.
Keywords: Endemic zoonoses; Risk model; Spatial data mining; Trypanosoma cruzi; Vector-borne diseases.
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