2022
DOI: 10.1101/2022.10.11.511693
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 87 publications
(111 reference statements)
0
2
0
Order By: Relevance
“…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).…”
Section: Considerations On Framework Methodology and Validationmentioning
confidence: 99%
See 1 more Smart Citation
“…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).…”
Section: Considerations On Framework Methodology and Validationmentioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%