Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/36984 |
Resumo: | Malaria was a major health problem in the first half of the 20th Century in mainland Portugal. Nowadays, although the disease is no longer endemic, there is still the risk of future endemic infections due to the continuous occurrence of imported cases and the possibility of transmission in the country by Anopheles atroparvus Van Thiel, 1927. Since vector abundance constitute one of the foremost factors in malaria transmission, we have created several habitat suitability models to describe this vector species' current distribution. Three different correlative models; namely (i) a multilayer perceptron artificial neural network (MLP-ANN); (ii) binary logistic regression (BLR); and (iii) Mahalanobis distance were used to combine the species records with a set of five environmental predictors. Kappa coefficient values from k-fold cross-validation records showed that binary logistic regression produced the best predictions, while the other two models also produced acceptable results. Therefore, in order to reduce uncertainty, the three suitability models were combined. The resulting model identified high suitability for An. atroparvus in the majority of the country with exception of the northern and central coastal areas. Malaria distribution during the last endemic period in the country was also compared with the combined suitability model, and a high degree of spatial agreement was obtained (kappa = 0.62). It was concluded that habitat suitability for malaria vectors can constitute valuable information on the assessment of several spatial attributes of the disease. In addition, the results suggest that the spatial distribution of An. atroparvus in the country remains very similar to the one known about seven decades ago. |
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Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland PortugalAnimalsMalariaModels, StatisticalNeural Networks (Computer)PortugalAnophelesEcosystemPopulation DensityMalaria was a major health problem in the first half of the 20th Century in mainland Portugal. Nowadays, although the disease is no longer endemic, there is still the risk of future endemic infections due to the continuous occurrence of imported cases and the possibility of transmission in the country by Anopheles atroparvus Van Thiel, 1927. Since vector abundance constitute one of the foremost factors in malaria transmission, we have created several habitat suitability models to describe this vector species' current distribution. Three different correlative models; namely (i) a multilayer perceptron artificial neural network (MLP-ANN); (ii) binary logistic regression (BLR); and (iii) Mahalanobis distance were used to combine the species records with a set of five environmental predictors. Kappa coefficient values from k-fold cross-validation records showed that binary logistic regression produced the best predictions, while the other two models also produced acceptable results. Therefore, in order to reduce uncertainty, the three suitability models were combined. The resulting model identified high suitability for An. atroparvus in the majority of the country with exception of the northern and central coastal areas. Malaria distribution during the last endemic period in the country was also compared with the combined suitability model, and a high degree of spatial agreement was obtained (kappa = 0.62). It was concluded that habitat suitability for malaria vectors can constitute valuable information on the assessment of several spatial attributes of the disease. In addition, the results suggest that the spatial distribution of An. atroparvus in the country remains very similar to the one known about seven decades ago.PAGEpressRepositório da Universidade de LisboaCapinha, CésarGomes, EduardoReis, EusébioRocha, JorgeSousa, Carla A.Do Rosário, V. E.Almeida, A. Paulo2019-02-13T14:40:49Z20092009-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/36984engCapinha, C., Gomes, E., Reis, E., Rocha, J., Sousa, C. A., do Rosário, V. E., Almeida, A. P. (2009). Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal. Geospatial Health, 3(2), pp. 177–187. https://doi.org/10.4081/gh.2009.2191827-198710.4081/gh.2009.219info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:33:54Zoai:repositorio.ul.pt:10451/36984Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:51:07.023656Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
title |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
spellingShingle |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal Capinha, César Animals Malaria Models, Statistical Neural Networks (Computer) Portugal Anopheles Ecosystem Population Density |
title_short |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
title_full |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
title_fullStr |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
title_full_unstemmed |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
title_sort |
Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal |
author |
Capinha, César |
author_facet |
Capinha, César Gomes, Eduardo Reis, Eusébio Rocha, Jorge Sousa, Carla A. Do Rosário, V. E. Almeida, A. Paulo |
author_role |
author |
author2 |
Gomes, Eduardo Reis, Eusébio Rocha, Jorge Sousa, Carla A. Do Rosário, V. E. Almeida, A. Paulo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Capinha, César Gomes, Eduardo Reis, Eusébio Rocha, Jorge Sousa, Carla A. Do Rosário, V. E. Almeida, A. Paulo |
dc.subject.por.fl_str_mv |
Animals Malaria Models, Statistical Neural Networks (Computer) Portugal Anopheles Ecosystem Population Density |
topic |
Animals Malaria Models, Statistical Neural Networks (Computer) Portugal Anopheles Ecosystem Population Density |
description |
Malaria was a major health problem in the first half of the 20th Century in mainland Portugal. Nowadays, although the disease is no longer endemic, there is still the risk of future endemic infections due to the continuous occurrence of imported cases and the possibility of transmission in the country by Anopheles atroparvus Van Thiel, 1927. Since vector abundance constitute one of the foremost factors in malaria transmission, we have created several habitat suitability models to describe this vector species' current distribution. Three different correlative models; namely (i) a multilayer perceptron artificial neural network (MLP-ANN); (ii) binary logistic regression (BLR); and (iii) Mahalanobis distance were used to combine the species records with a set of five environmental predictors. Kappa coefficient values from k-fold cross-validation records showed that binary logistic regression produced the best predictions, while the other two models also produced acceptable results. Therefore, in order to reduce uncertainty, the three suitability models were combined. The resulting model identified high suitability for An. atroparvus in the majority of the country with exception of the northern and central coastal areas. Malaria distribution during the last endemic period in the country was also compared with the combined suitability model, and a high degree of spatial agreement was obtained (kappa = 0.62). It was concluded that habitat suitability for malaria vectors can constitute valuable information on the assessment of several spatial attributes of the disease. In addition, the results suggest that the spatial distribution of An. atroparvus in the country remains very similar to the one known about seven decades ago. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009 2009-01-01T00:00:00Z 2019-02-13T14:40:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/36984 |
url |
http://hdl.handle.net/10451/36984 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Capinha, C., Gomes, E., Reis, E., Rocha, J., Sousa, C. A., do Rosário, V. E., Almeida, A. P. (2009). Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal. Geospatial Health, 3(2), pp. 177–187. https://doi.org/10.4081/gh.2009.219 1827-1987 10.4081/gh.2009.219 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
PAGEpress |
publisher.none.fl_str_mv |
PAGEpress |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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