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Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
by
Glidden, Caroline K.
, Castellanos, Adrian A.
, Murran, Aisling Roya
, Han, Barbara A.
, Mordecai, Erin A.
, Silva, Rafaella Albuquerque
in
Agricultural land
/ Animals
/ Animals, Wild
/ Bacterial infections
/ Biogeography
/ Biology and Life Sciences
/ Cladistics
/ Climate
/ Climate change
/ Computer and Information Sciences
/ Diagnosis
/ Disease susceptibility
/ Distribution
/ Earth Sciences
/ Ecology and Environmental Sciences
/ Environmental changes
/ Environmental impact
/ Evaluation
/ Global warming
/ Health risks
/ Host range
/ Hosts
/ Humans
/ Infections
/ Infectious diseases
/ Land cover
/ Land use
/ Leishmania
/ Leishmania - genetics
/ Leishmaniasis
/ Leishmaniasis - epidemiology
/ Leishmaniasis - veterinary
/ Machine learning
/ Mammals
/ Medicine and Health Sciences
/ Parasites
/ Parasitic diseases
/ Pathogens
/ Phlebotomus - parasitology
/ Phylogenetics
/ Phylogeny
/ Physiology
/ Protozoa
/ Psychodidae - parasitology
/ Spatial distribution
/ Surveillance
/ Taxonomy
/ Temporal distribution
/ Transmission
/ Tropical diseases
/ Urban agriculture
/ Vector-borne diseases
/ Wildlife
/ Zoonoses
2023
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Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
by
Glidden, Caroline K.
, Castellanos, Adrian A.
, Murran, Aisling Roya
, Han, Barbara A.
, Mordecai, Erin A.
, Silva, Rafaella Albuquerque
in
Agricultural land
/ Animals
/ Animals, Wild
/ Bacterial infections
/ Biogeography
/ Biology and Life Sciences
/ Cladistics
/ Climate
/ Climate change
/ Computer and Information Sciences
/ Diagnosis
/ Disease susceptibility
/ Distribution
/ Earth Sciences
/ Ecology and Environmental Sciences
/ Environmental changes
/ Environmental impact
/ Evaluation
/ Global warming
/ Health risks
/ Host range
/ Hosts
/ Humans
/ Infections
/ Infectious diseases
/ Land cover
/ Land use
/ Leishmania
/ Leishmania - genetics
/ Leishmaniasis
/ Leishmaniasis - epidemiology
/ Leishmaniasis - veterinary
/ Machine learning
/ Mammals
/ Medicine and Health Sciences
/ Parasites
/ Parasitic diseases
/ Pathogens
/ Phlebotomus - parasitology
/ Phylogenetics
/ Phylogeny
/ Physiology
/ Protozoa
/ Psychodidae - parasitology
/ Spatial distribution
/ Surveillance
/ Taxonomy
/ Temporal distribution
/ Transmission
/ Tropical diseases
/ Urban agriculture
/ Vector-borne diseases
/ Wildlife
/ Zoonoses
2023
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Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
by
Glidden, Caroline K.
, Castellanos, Adrian A.
, Murran, Aisling Roya
, Han, Barbara A.
, Mordecai, Erin A.
, Silva, Rafaella Albuquerque
in
Agricultural land
/ Animals
/ Animals, Wild
/ Bacterial infections
/ Biogeography
/ Biology and Life Sciences
/ Cladistics
/ Climate
/ Climate change
/ Computer and Information Sciences
/ Diagnosis
/ Disease susceptibility
/ Distribution
/ Earth Sciences
/ Ecology and Environmental Sciences
/ Environmental changes
/ Environmental impact
/ Evaluation
/ Global warming
/ Health risks
/ Host range
/ Hosts
/ Humans
/ Infections
/ Infectious diseases
/ Land cover
/ Land use
/ Leishmania
/ Leishmania - genetics
/ Leishmaniasis
/ Leishmaniasis - epidemiology
/ Leishmaniasis - veterinary
/ Machine learning
/ Mammals
/ Medicine and Health Sciences
/ Parasites
/ Parasitic diseases
/ Pathogens
/ Phlebotomus - parasitology
/ Phylogenetics
/ Phylogeny
/ Physiology
/ Protozoa
/ Psychodidae - parasitology
/ Spatial distribution
/ Surveillance
/ Taxonomy
/ Temporal distribution
/ Transmission
/ Tropical diseases
/ Urban agriculture
/ Vector-borne diseases
/ Wildlife
/ Zoonoses
2023
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Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
Journal Article
Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis
2023
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Overview
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 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 to limit pathogen spread and the ability to accurately predict 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 that 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 environmental change on local transmission cycles.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
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