Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.
BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence...
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ftdoajarticles:oai:doaj.org/article:08bf7e519bcc472486f31c16f12fd269 2023-05-15T15:05:55+02:00 Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. Dimitrios-Alexios Karagiannis-Voules Ronaldo G C Scholte Luiz H Guimarães Jürg Utzinger Penelope Vounatsou 2013-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0002213 https://doaj.org/article/08bf7e519bcc472486f31c16f12fd269 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC3649962?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 doi:10.1371/journal.pntd.0002213 1935-2727 1935-2735 https://doaj.org/article/08bf7e519bcc472486f31c16f12fd269 PLoS Neglected Tropical Diseases, Vol 7, Iss 5, p e2213 (2013) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2013 ftdoajarticles https://doi.org/10.1371/journal.pntd.0002213 2022-12-31T04:40:10Z BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) PLoS Neglected Tropical Diseases 7 5 e2213 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Dimitrios-Alexios Karagiannis-Voules Ronaldo G C Scholte Luiz H Guimarães Jürg Utzinger Penelope Vounatsou Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence. |
format |
Article in Journal/Newspaper |
author |
Dimitrios-Alexios Karagiannis-Voules Ronaldo G C Scholte Luiz H Guimarães Jürg Utzinger Penelope Vounatsou |
author_facet |
Dimitrios-Alexios Karagiannis-Voules Ronaldo G C Scholte Luiz H Guimarães Jürg Utzinger Penelope Vounatsou |
author_sort |
Dimitrios-Alexios Karagiannis-Voules |
title |
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
title_short |
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
title_full |
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
title_fullStr |
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
title_full_unstemmed |
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil. |
title_sort |
bayesian geostatistical modeling of leishmaniasis incidence in brazil. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2013 |
url |
https://doi.org/10.1371/journal.pntd.0002213 https://doaj.org/article/08bf7e519bcc472486f31c16f12fd269 |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Arctic Laplace |
geographic_facet |
Arctic Laplace |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 7, Iss 5, p e2213 (2013) |
op_relation |
http://europepmc.org/articles/PMC3649962?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 doi:10.1371/journal.pntd.0002213 1935-2727 1935-2735 https://doaj.org/article/08bf7e519bcc472486f31c16f12fd269 |
op_doi |
https://doi.org/10.1371/journal.pntd.0002213 |
container_title |
PLoS Neglected Tropical Diseases |
container_volume |
7 |
container_issue |
5 |
container_start_page |
e2213 |
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1766337611979292672 |