Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

Abstract

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 zoonotic Leishmania system is highly complex: Leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, 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 and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions limiting pathogen spread as well as accurately predicting the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife 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 predicted Leishmania host 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 zoonotic Leishmania hosts 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 global change on local transmission cycles.

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

“…We employed nested cross-validation to estimate generalization error of our model ensemble in a robust and unbiased way. As highlighted by a recent application to the prediction of Leishmania hosts (Glidden et al, 2023), nested-cross validation is an e cient resampling strategy to obtain performance estimates when using small datasets. It allows both model training and selection within the same resampling regime without causing over tting, as opposed to non-nested cross-validation where ne-tuning could bias the model towards the dataset, potentially yielding overly optimistic performance estimates (Cawley & Talbot, 2010).…”

“…Trait-based statistical models can prove useful to rapidly identify potential reservoir species and pinpoint priorities for zoonotic surveillance across host taxa and geographical locations without necessarily having to perform expensive and labour-intensive eld investigations. Approaches that employ these methods have been used in a wide array of contexts and disease systems, such as the identi cation of targets for Nipah virus surveillance in India (Plowright et al, 2019), and the recognition of unknown hosts of zoonotic leishmaniasis (Glidden et al, 2023).…”