Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil

Submitted: 7 April 2022
Accepted: 20 May 2022
Published: 8 June 2022
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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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Anderson MC, Zolin CA, Sentelhas PC, Hain CR, Semmens K, Yilmaz MT, Gao F, Otkin JA, Tetrault R, 2016. The evaporative stress index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impact. Rem Sens Environ 174:82-99. DOI: https://doi.org/10.1016/j.rse.2015.11.034
Boser A, Sousa D, Larsen A, MacDonald A. 2021. Micro-climate to macro-risk: mapping fine scale differences in mosquito-borne disease risk using remote sensing. Environ Res Lett 16:124014. DOI: https://doi.org/10.1088/1748-9326/ac3589
Casanova C, Andrighetti MTM, Sampaio SMP, Marcoris MLG, Colla-Jacques FE. 2013. Larval breeding sites of Lutzomyia longipalpis (Diptera:Psychodidae) in visceral leishmaniasis endemic urban areas in southeastern Brazil. PLoS Negl Trop Dis 7:e2443. DOI: https://doi.org/10.1371/journal.pntd.0002443
Cardim MFM, Rodas LC, Dibo MR, Guirado MM, Oliveira AM, Chiaravalloti-Neto F, 2013. Introduction and expansion of human American visceral leishmaniasis in the state of Sao Paulo, Brazil, 1999-2011. Rev Saude Publica 47:691-700. DOI: https://doi.org/10.1590/S0034-8910.2013047004454
Colliander A, Jackson TJ, Bindlish R, Chan N, Kim SB, Cosh RB, Dunbar RS, Dang L, Pashaian I, Asanuma J, Aida K, Berg A, Rowlandson T, Bosch D, Caldwell T, Caylor K, Goodrich D, Jassar H, Lopez-Baeza E, Martínez-Fernández J, González-Zamora A, Livingston S, McNairn J, Pacheco A, Moghaddam M, Montzka C, Notarnicola C, Niedris G, Pellarin T, Prueger J, Pulliainen J, Rautiainen K, Ramos J, Seyfried M, Starks P, Su Z, Zeng Y, van der 7 Velde R, Thibeault M, Dorigo W, Vreugdenhil M, Walker JP, Wu X, Monerris A, O’Neill PE, Entekhabi D, Njoku EG, Yueh S, 2017. Validation of SMAP surface soil moisture products with core validation sites. Rem Sens Environ 191:215-31. DOI: https://doi.org/10.1016/j.rse.2017.01.021
Fick SE, Hijmans RJ, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 190:231-59. DOI: https://doi.org/10.1002/joc.5086
IBGE, Instituto Brasileiro de Geografia e Estatistica, 2020. Mapas político-administrativos Estaduais. IBGE, Brazil.
Hess A, Davis JK, Wimberly MC. 2018. Identifying environmental risk factors and mapping the distribution of West Nile virus in an endemic region of North America. Geohealth 2:395-409. DOI: https://doi.org/10.1029/2018GH000161
Hosseinian SS, Yousefkhani, Rastegar-Pouyani E, Aliabadian M, 2016. Ecological niche differentiation and taxonomic distinction between Eremias strauchi and Eremias strauchi kopetdaghica (Squamata: Lacertidae) on the Iranian Plateau based on ecological niche modeling. Ital J Zool 83:408-16. DOI: https://doi.org/10.1080/11250003.2016.1209581
Lima ID, Lima ALM, Mendes-Aguiar CDO, Coutinho JFV, Wilson ME, Pearson RD, 2018. Changing demographics of visceral leishmaniasis in northeast Brazil: Lessons for the future. PLoS Negl Trop Dis 12:e0006164. DOI: https://doi.org/10.1371/journal.pntd.0006164
Malone JB, Bergquist NR, 2012. Mapping and modelling neglected tropical diseases and poverty in Latin America and the Caribbean. Geospat Health 6:S1-5. DOI: https://doi.org/10.4081/gh.2012.115
Malone JB, Bergquist, NR, Martins M, Luvall JC, 2019. Use of geospatial surveillance and response systems for vector borne diseases in the elimination phase. Trop Med Infect Dis 4:15-31. DOI: https://doi.org/10.3390/tropicalmed4010015
Miranda MJ, Pinto HS, Junior JZ, Fagundes RM, Fonsechi DB, Calve L, Pellegrino GQ, 2014. A classificacao climaticade Koeppen para o estado de Sao Paulo. Centro de Pesquisas Meteorológicas e Aplicadas à Agricultura (CEPAGRI). Archived from the original on 19 February 2014.
National Academies of Sciences, Engineering, and Medicine, 2018. Thriving on our changing planet: a decadal strategy for earth observation from space. The National Academies Press Washington, DC, USA.
Nieto P, Malone JB, Bavia ME, 2006. Ecological niche modeling for visceral leishmaniasis in the state of Bahia, Brazil, using genetic algorithm for rule-set prediction and growing degree day-water budget analysis. Geospat Health 1:115-26. DOI: https://doi.org/10.4081/gh.2006.286
Phillips SJ, Anderson RP, Schapire RE, 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190:231-59. DOI: https://doi.org/10.1016/j.ecolmodel.2005.03.026
Prestes-Carneiro LE, Daniel LAF, D’Andrea LZ, Vieira AG, Anjolete IR Andre L, Flores EF, 2019. Spatiotemporal analysis and environmental risk factors of visceral leishmaniasis in an urban setting in São Paulo State, Brazil. Parasites Vect 2:251. DOI: https://doi.org/10.1186/s13071-019-3496-6
Rodgers MM, Bavia ME, Fonseca EOL, Cova B, Nascimento MM, Carniero DD, Cardim LL, Malone JB, 2019. Ecological Niche models for sand fly species and predicted distribution of Lutzomyia longipalpiis (Diptera: Psychodidae) and visceral leishmaniasis in Bahia state, Brazil. Envir Monit Assess 191:331. DOI: https://doi.org/10.1007/s10661-019-7431-2
Scavuzzo JM, Trucco F, Espinosa M, Tauro CB, Abril M, Scavuzzo CM, Frery AC. 2021. Modeling Dengue vector population using remotely sensed data and machine learning. Acta Tropica 185:167-75. DOI: https://doi.org/10.1016/j.actatropica.2018.05.003
Schoener TW, 1968. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49:704-26. DOI: https://doi.org/10.2307/1935534
Secretaria de Saúde do Estado da Bahia, 2018. Superintendência de Vigilância e Proteção da Saúde. Informe Epidemiológico de Leishmaniose Visceral (LV) - Bahia. Portal de Vigilância em saúde / Boletins Epidemiológicos e Notas técnicas / Leishmaniose, n. 01, fev. 2018. Available from: http://www.saude.ba.gov.br/wp-content/uploads/2017/11/2018-Boletim-deLeishmaniose-Vivsceral-n.-01.pdf Accessed: 17 June 2018.
Sevá AP, Mao L, Galvis-Ovallos F, Tucker Lima JM, Valle D, 2017. Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil. PLoS Negl Trop Dis 11:e0005353. DOI: https://doi.org/10.1371/journal.pntd.0005353
Warren CP, Pascual M, Lafferty KD, Kuris AM, 2010. The inverse niche model for food webs with parasites. Theor Ecol 3:285-94. DOI: https://doi.org/10.1007/s12080-009-0069-x
Warren DL, Glor RE, Turelli M, 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868-83. DOI: https://doi.org/10.1111/j.1558-5646.2008.00482.x
Warren DL, Seifert SN, 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335-42. DOI: https://doi.org/10.1890/10-1171.1

How to Cite

Rodgers, M. de S. M., Fonseca, E., Nieto, P. del M., Malone, J. B., Luvall, J. C., McCarroll, J. C., Avery, R. H., Bavia, M. E., Guimaraes, R., Wen, X. ., Silva, M. M. N., Carneiro, D. D., & Cardim, L. L. (2022). 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, 17(1). https://doi.org/10.4081/gh.2022.1095

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