Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome

A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the f...

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Autores:
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22824
Acceso en línea:
https://doi.org/10.1371/journal.pntd.0007629
https://repository.urosario.edu.co/handle/10336/22824
Palabra clave:
Altitude
Anthropology
Biomass
Biome
Climate
Disease transmission
Ecological niche
Ecological phenomena and functions
Environmental temperature
Geographic distribution
Human
Human footprint
Incidence
Neotropics
Population density
Poverty
Prediction
Seasonal variation
Skin leishmaniasis
Species richness
Tropical rain forest
Ecology
Ecosystem
Forest
French guiana
Prevalence
Season
Skin leishmaniasis
South america
Ecology
Ecosystem
Forests
French guiana
Humans
Prevalence
Seasons
South america
cutaneous
Leishmaniasis
Rights
License
Abierto (Texto Completo)
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dc.title.spa.fl_str_mv Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
title Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
spellingShingle Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
Altitude
Anthropology
Biomass
Biome
Climate
Disease transmission
Ecological niche
Ecological phenomena and functions
Environmental temperature
Geographic distribution
Human
Human footprint
Incidence
Neotropics
Population density
Poverty
Prediction
Seasonal variation
Skin leishmaniasis
Species richness
Tropical rain forest
Ecology
Ecosystem
Forest
French guiana
Prevalence
Season
Skin leishmaniasis
South america
Ecology
Ecosystem
Forests
French guiana
Humans
Prevalence
Seasons
South america
cutaneous
Leishmaniasis
title_short Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
title_full Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
title_fullStr Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
title_full_unstemmed Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
title_sort Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
dc.subject.keyword.spa.fl_str_mv Altitude
Anthropology
Biomass
Biome
Climate
Disease transmission
Ecological niche
Ecological phenomena and functions
Environmental temperature
Geographic distribution
Human
Human footprint
Incidence
Neotropics
Population density
Poverty
Prediction
Seasonal variation
Skin leishmaniasis
Species richness
Tropical rain forest
Ecology
Ecosystem
Forest
French guiana
Prevalence
Season
Skin leishmaniasis
South america
Ecology
Ecosystem
Forests
French guiana
Humans
Prevalence
Seasons
South america
topic Altitude
Anthropology
Biomass
Biome
Climate
Disease transmission
Ecological niche
Ecological phenomena and functions
Environmental temperature
Geographic distribution
Human
Human footprint
Incidence
Neotropics
Population density
Poverty
Prediction
Seasonal variation
Skin leishmaniasis
Species richness
Tropical rain forest
Ecology
Ecosystem
Forest
French guiana
Prevalence
Season
Skin leishmaniasis
South america
Ecology
Ecosystem
Forests
French guiana
Humans
Prevalence
Seasons
South america
cutaneous
Leishmaniasis
dc.subject.keyword.eng.fl_str_mv cutaneous
Leishmaniasis
description A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America. © 2019 Chavy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
publishDate 2019
dc.date.created.spa.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-05-25T23:58:13Z
dc.date.available.none.fl_str_mv 2020-05-25T23:58:13Z
dc.type.eng.fl_str_mv article
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dc.identifier.doi.none.fl_str_mv https://doi.org/10.1371/journal.pntd.0007629
dc.identifier.issn.none.fl_str_mv 19352727
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https://repository.urosario.edu.co/handle/10336/22824
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dc.language.iso.spa.fl_str_mv eng
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dc.relation.citationIssue.none.fl_str_mv No. 8
dc.relation.citationTitle.none.fl_str_mv PLoS Neglected Tropical Diseases
dc.relation.citationVolume.none.fl_str_mv Vol. 13
dc.relation.ispartof.spa.fl_str_mv PLoS Neglected Tropical Diseases, ISSN:19352727, 19352735, Vol.13, No.8 (2019)
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spelling 605a289a-2fd8-40ed-aee6-68ad30760c175dd7ee03-4371-4fef-a1fa-65de2bb3fa85c3f63f47-7945-4c97-9544-82b3de6b288f101171611860007f5f951-ff31-4b0f-94f0-7492d0a86f059920ff30-55c2-45ef-abbd-125d55927f0f8511d064-2768-4c77-92fe-4b8ac03a1f9259dd2393-89a7-4c8f-ade4-a10d87ffac476ad7d386-a8fa-4e4f-a544-f7c54a55774b8b6261d5-6cb4-4b3e-98b9-8ebd79f45b130cb59143-d5b2-47a6-be58-5c70d9b25d8d2020-05-25T23:58:13Z2020-05-25T23:58:13Z2019A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America. © 2019 Chavy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.application/pdfhttps://doi.org/10.1371/journal.pntd.00076291935272719352735https://repository.urosario.edu.co/handle/10336/22824engPublic Library of ScienceNo. 8PLoS Neglected Tropical DiseasesVol. 13PLoS Neglected Tropical Diseases, ISSN:19352727, 19352735, Vol.13, No.8 (2019)https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071281120&doi=10.1371%2fjournal.pntd.0007629&partnerID=40&md5=f03ac3ae5702f29fe49d0a63e2791651Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURAltitudeAnthropologyBiomassBiomeClimateDisease transmissionEcological nicheEcological phenomena and functionsEnvironmental temperatureGeographic distributionHumanHuman footprintIncidenceNeotropicsPopulation densityPovertyPredictionSeasonal variationSkin leishmaniasisSpecies richnessTropical rain forestEcologyEcosystemForestFrench guianaPrevalenceSeasonSkin leishmaniasisSouth americaEcologyEcosystemForestsFrench guianaHumansPrevalenceSeasonsSouth americacutaneousLeishmaniasisEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biomearticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Chavy, AgatheNava, Alessandra Ferreira DalesLuz, Sergio Luiz BessaRamírez, Juan DavidHerrera, Giovannydos Santos, Thiago VasconcelosGinouves, MarineDemar, MagaliePrévot, GhislaineGuégan, Jean-Françoisde Thoisy, BenoîtORIGINALjournal-pntd-0007629.pdfapplication/pdf2626986https://repository.urosario.edu.co/bitstreams/71510ca0-026d-4484-a3fb-ad964e3d85dd/downloadfa320c827ffbbf82d30018dab454c363MD51TEXTjournal-pntd-0007629.pdf.txtjournal-pntd-0007629.pdf.txtExtracted texttext/plain77465https://repository.urosario.edu.co/bitstreams/2757628a-3666-4b81-98b7-e9ff8de0248e/download4fd03fbc88b34f264c14ad2880c16e2dMD52THUMBNAILjournal-pntd-0007629.pdf.jpgjournal-pntd-0007629.pdf.jpgGenerated Thumbnailimage/jpeg4556https://repository.urosario.edu.co/bitstreams/a585433d-e249-4c54-b843-b446bd8eb9f2/downloadaae57cdb227f465754fa20722dcce405MD5310336/22824oai:repository.urosario.edu.co:10336/228242022-08-31 07:38:15.157https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co