Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
Authors: Glidden Caroline K. , Murran Aisling , Silva Rafaella Albuquerque , Castellanos Adrian A. , Han Barbara A. , Mordecai Erin 
Published: October 2022

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonoticLeishmaniasystem is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 differentLeishmaniaspecies, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually andLeishmaniatransmission to humans occurs in absence of a known host. As such, the full range ofLeishmaniahosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predicting the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonoticLeishmaniawildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predictedLeishmaniahost status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonoticLeishmaniahosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles.

Author summary

Leishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has targeted leishmaniasis for elimination and control by 2030, the disease remains a persistent threat. Across the Americas, particularly in Central America, the southeastern United States, and perimeters of the Amazon Basin, risk of infection is increasing in geographic extent and elevation. While it is known thatLeishmaniaparasites, the causative agent of leishmaniasis, are maintained in the environment via a mammalian host, the full suite of wildlife hosts has yet to be documented, which significantly hinders control efforts. Here, we use machine learning and ecological and evolutionary trait profiles of known hosts to identify unrecognized potential wildlife hosts ofLeishmania. We identify 136 mammals in the Americas that are likely to be exposed to and infected by zoonoticLeishmaniain the wild. The high number of unrecognized potential hosts emphasizes a need to better invest in studying the ecological epidemiology of leishmaniasis. The study provides information and tools to support targeted intervention and management of this important poverty-associated disease.