Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil
Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is...
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2022-06-01
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| Series: | Geospatial Health |
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| Online Access: | https://www.geospatialhealth.net/index.php/gh/article/view/1095 |
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| author | Moara de Santana Martins Rodgers Elivelton Fonseca Prixia del Mar Nieto John B. Malone Jeffery C. Luvall Jennifer C. McCarroll Ryan Harry Avery Maria Emilia Bavia Raul Guimaraes Xue Wen Marta Mariana Nascimento Silva Deborah D.M.T. Carneiro Luciana Lobato Cardim |
| author_facet | Moara de Santana Martins Rodgers Elivelton Fonseca Prixia del Mar Nieto John B. Malone Jeffery C. Luvall Jennifer C. McCarroll Ryan Harry Avery Maria Emilia Bavia Raul Guimaraes Xue Wen Marta Mariana Nascimento Silva Deborah D.M.T. Carneiro Luciana Lobato Cardim |
| author_sort | Moara de Santana Martins Rodgers |
| collection | DOAJ |
| description |
Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.
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| first_indexed | 2024-12-12T13:05:55Z |
| format | Article |
| id | doaj.art-e09f760fdc4248a8b5d43505f836ede1 |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-12-12T13:05:55Z |
| publishDate | 2022-06-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-e09f760fdc4248a8b5d43505f836ede12022-12-22T00:23:40ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962022-06-0117110.4081/gh.2022.1095Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, BrazilMoara de Santana Martins Rodgers0Elivelton Fonseca1Prixia del Mar Nieto2John B. Malone3Jeffery C. Luvall4Jennifer C. McCarroll5Ryan Harry Avery6Maria Emilia Bavia7Raul Guimaraes8Xue Wen9Marta Mariana Nascimento Silva10Deborah D.M.T. Carneiro11Luciana Lobato Cardim12Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LASao Paulo State University, Presidente PrudentePathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LAPathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LANASA Marshall Space Flight Center, Huntsville, ALPathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LAPathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LAFederal University of Bahia, SalvadorSao Paulo State University, Presidente PrudentePathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LAFederal University of Bahia, SalvadorFederal University of Bahia, SalvadorFederal University of Bahia, Salvador Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk. https://www.geospatialhealth.net/index.php/gh/article/view/1095LeishmaniasisLutzomyia longipalpissoil moisture active passive satelliteecological niche modelWorldClim 2.0. |
| spellingShingle | Moara de Santana Martins Rodgers Elivelton Fonseca Prixia del Mar Nieto John B. Malone Jeffery C. Luvall Jennifer C. McCarroll Ryan Harry Avery Maria Emilia Bavia Raul Guimaraes Xue Wen Marta Mariana Nascimento Silva Deborah D.M.T. Carneiro Luciana Lobato Cardim Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil Geospatial Health Leishmaniasis Lutzomyia longipalpis soil moisture active passive satellite ecological niche model WorldClim 2.0. |
| title | Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil |
| title_full | Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil |
| title_fullStr | Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil |
| title_full_unstemmed | Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil |
| title_short | Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil |
| title_sort | use of soil moisture active passive satellite data and worldclim 2 0 data to predict the potential distribution of visceral leishmaniasis and its vector em lutzomyia longipalpis em in sao paulo and bahia states brazil |
| topic | Leishmaniasis Lutzomyia longipalpis soil moisture active passive satellite ecological niche model WorldClim 2.0. |
| url | https://www.geospatialhealth.net/index.php/gh/article/view/1095 |
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