ArticlePathogens and global health2021
Predicted distribution of sand fly (Diptera: Psychodidae) species involved in the transmission of Leishmaniasis in São Paulo state, Brazil, utilizing maximum entropy ecological niche modeling.
Article in Pathogens and global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Factors Associated With the Emergence and Spread of Visceral Leishmaniasis in the State of São Paulo, Brazil.Zoonoses and public health · 2026Article
- Fragments of urban forest in a large Amazonian city as habitats for potential Leishmania vectors.Parasite epidemiology and control · 2026Article
- Circulation aspects of phlebotomines (Diptera:Journal of parasitic diseases : official organ of the Indian Society for Parasitology · 2025Article
- Geospatial technologies for targeting priority areas on surveillance and response of visceral leishmaniasis in São Paulo state, Brazil: embracing a One Health integrative approach.Journal of global health · 2025Article
- Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases.Tropical medicine and infectious disease · 2023Review
- Integration of phlebotomine ecological niche modelling, and mapping of cutaneous leishmaniasis surveillance data, to identify areas at risk of under-estimation.Acta tropica · 2021Article
- Cases and distribution of visceral leishmaniasis in western São Paulo: A neglected disease in this region of Brazil.PLoS neglected tropical diseases · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leishmaniasis is a public health problem worldwide. We aimed to predict ecological niche models (ENMs) for visceral (VL) and cutaneous (CL) leishmaniasis and the sand flies involved in the transmission of leishmaniasis in São Paulo, Brazil. Phlebotomine sand flies were collected between 1985 and 2015. ENMs were created for each sand fly species using Maximum Entropy Species Distribution Modeling software, and 20 climatic variables were determined.
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