Ecological characterization of a cutaneous leishmaniasis outbreak through remotely sensed land cover changes

Submitted: 30 June 2021
Accepted: 16 November 2021
Published: 6 May 2022
Abstract Views: 1091
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In this work we assessed the environmental factors associated with the spatial distribution of a cutaneous leishmaniasis (CL) outbreak during 2015-2016 in north-eastern Argentina to understand its typical or atypical eco-epidemiological pattern. We combined locations of human CL cases with relevant predictors derived from analysis of remote sensing imagery in the framework of ecological niche modelling and trained MaxEnt models with cross-validation for predictors estimated at different buffer areas relevant to CL vectors (50 and 250 m radii). To account for the timing of biological phenomena, we considered environmental changes occurring in two periods, 2014-2015 and 2015-2016. The remote sensing analysis identified land cover changes in the surroundings of CL cases, mostly related to new urbanization and flooding. The distance to such changes was the most important variable in most models. The weighted average map denoted higher suitability for CL in the outskirts of the city of Corrientes and in areas close to environmental changes. Our results point to a scenario consistent with a typical CL outbreak, i.e. changes in land use or land cover are the main triggering factor and most affected people live or work in border habitats.

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Acosta-Soto L, Encinas ES, Deschutter EJ, Pasetto RAL, Petri de Odriozola EMA, Bornay-Llinares FJ, Ramos-Rincón JM, 2020. Autochthonous Outbreak of Cutaneous Leishmaniasis due to Leishmania infantum in Corrientes Province, Argentina. Am J Trop Med Hyg 102:593-7.
Arana MD, Martínez GA, Oggero AJ, Natale ES, Morrone JJ, 2017. Map and Shapefile of the Bio-geographic Provinces of Argentina. Zootaxa 4341:420-2.
Araújo MB, New M, 2007. Ensemble forecasting of species distributions. Trends Ecol Evol 22:42-7.
Berrozpe P, Lamattina D, Santini MS, Araujo AV, Utgés ME, Salomón OD, 2017. Environmental suitability for Lutzomyia longipalpis in a subtropical city with a recently established visceral leishmaniasis transmission cycle, Argentina. Mem I Oswaldo Cruz 112:674-80.
Berrozpe PE, Lamattina D, Santini MS, Araujo AV, Torrusio SE, Salomón OD, 2019. Spatiotemporal dynamics of Lutzomyia longipalpis and macro-habitat characterization using satellite images in a leishmaniasis-endemic city in Argentina. Med Vet Entomol 33:89-98.
Breiman L, 2001. Random Forests. Machine Learning 45:5-32.
Bruhn FRP, Morais MHF, Cardoso DL, Bruhn NCP, Ferreira F, Rocha CMBM, 2018. Spatial and temporal relationships between human and canine visceral leishmaniases in Belo Horizonte, Mi-nas Gerais, 2006-2013. Parasite Vector 11:372.
Chanampa M del M, Gleiser RM, Hoyos CL, Copa GN, Mangudo C, Nasser JR, Gil JF, 2018. Vege-tation Cover and Microspatial Distribution of Sand Flies (Diptera: Psychodidae) in an Endemic Locality for Cutaneous Leishmaniasis in Northern Argentina. J Med Entomol 55:1431-9.
Chavy A, Nava AFD, Luz SLB, Ramírez JD, Herrera G, Santos TV dos, Ginouves M, Demar M, Prévot G, Guégan J-F, Thoisy B de, 2019. Ecological niche modeling for predicting the risk of Cutaneous Leishmaniasis in the Neotropical moist forest biome. PLOS Neglect Trop D 13:e0007629.
Correa Antonialli SA, Torres TG, Paranhos Filho AC, Tolezano JE (2007) Spatial analysis of Ameri-can Visceral Leishmaniasis in Mato Grosso do Sul State, Central Brazil. J Infection 54:509-14.
Fernández MS, Santini MS, Cavia R, Sandoval AE, Pérez AA, Acardi S, Salomón OD, 2013. Spatial and temporal changes in Lutzomyia longipalpis abundance, a Leishmania infantum vector in an urban area in northeastern Argentina. Mem I Oswaldo Cruz 108:817-24.
Ferreyra de Souza RA, Andreoli RV, Kayano MT, Carvalho AL, 2015. American Cutaneous Leish-maniasis cases in the metropolitan region of Manaus, Brazil: association with climate variables over time. Geospatial Health 10:40-7.
Gómez-Bravo A, German A, Abril M, Scavuzzo M, Salomón OD, 2017. Spatial population dynamics and temporal analysis of the distribution of Lutzomyia longipalpis (Lutz & Neiva, 1912) (Dip-tera: Psychodidae: Phlebotominae) in the city of Clorinda, Formosa, Argentina. Parasite Vector 10:352.
Gouveia C, de Oliveira RM, Zwetsch A, Motta-Silva D, Carvalho BM, de Santana AF, Rangel EF, 2012. Integrated Tools for American Cutaneous Leishmaniasis Surveillance and Control: Inter-vention in an Endemic Area in Rio de Janeiro, RJ, Brazil. Interdiscipl Perspect Infect Dis 2012:568312.
GRASS Development Team, 2020. Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.8. Open Source Geospatial Foundation. Available from: http://grass.osgeo.org
Gutiérrez-Torres JD, 2020. Temporal lagged relationship between a vegetation index and cutaneous leishmaniasis cases in Colombia: an analysis implementing a distributed lag nonlinear model. Parasitol Res 119:1075-82.
Hernandez PA, Graham CH, Master LL, Albert DL, The ADL, 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecog-raphy 5:773-85.
Hotez PJ, 2018. Human Parasitology and Parasitic Diseases: Heading Towards 2050. Adv Parasit 100:29-38.
INDEC, 2010. Censo Nacional de Población, Hogares y Viviendas. Instituto Nacional de Estadística y Censo. Available from: https://www.indec.gob.ar/indec/web/Nivel4-CensoProvincia-3-999-18-021-2010
Joshi A, Miller C, 2021. Review of machine learning techniques for mosquito control in urban envi-ronments. Ecol Inform 61:101241.
Liu C, Berry PM, Dawson TP, Pearson RG, 2005. Selecting thresholds of occurrence in the predic-tion of species distributions. Ecography 28:385-93.
Liu C, White M, Newell G, 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778-89.
Liu Q, Liu G, Huang C, Liu S, Zhao J, 2014. A tasseled cap transformation for Landsat 8 OLI TOA reflectance images. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, pp 541-544.
Malila WA, 1980. Change vector analysis: an approach for detecting forest changes with Landsat. LARS Symposia 326-335.
Martínez MF, Kowalewski MM, Giuliani MG, Acardi SA, Salomón OD, 2020. Molecular characteri-zation of Leishmania species in free-ranging howler monkeys in northeastern Argentina. Acta Trop 210:105534.
Merow C, Smith MJ, Silander JA, 2013. A practical guide to MaxEnt for modeling species’ distribu-tions: what it does, and why inputs and settings matter. Ecography 36:1058-69.
Ministerio de Salud, 2007. Manual de normas y procedimientos de Vigilancia y Control de Enferme-dades de Notificación Obligatoria. Ministerio de Salud de la República Argentina, Buenos Aires, Argentina.
Moya SL, Giuliani MG, Santini MS, Quintana MG, Salomón OD, Liotta DJ, 2017. Leishmania in-fantum DNA detected in phlebotomine species from Puerto Iguazú City, Misiones province, Ar-gentina. Acta Trop 172:122-4.
Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG, 2014. Where is positional uncertain-ty a problem for species distribution modelling? Ecography 37:191-203.
PAHO, 2019. Leishmaniases. Epidemiological Report of the Americas, December 2019. PAHO, Washington, D.C.
Pasquali AKS, Baggio RA, Boeger WA, González-Britez N, Guedes DC, Chaves EC, Thomaz-Soccol V, 2019. Dispersion of Leishmania (Leishmania) infantum in central-southern Brazil: Evidence from an integrative approach. PLOS Neglect Trop D 13:e0007639.
Phillips S, Anderson R, Schapire R, 2006. Maximum Entropy Modeling of Species Geographic Dis-tributions. Ecoll Model 190:231-59.
Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME, 2017. Opening the black box: an open-source release of Maxent. Ecography 40:887-93.
QGIS Development Team, 2019. QGIS Geographic Information System. Open Source Geospatial Foundation. Available from: http://qgis.osgeo.org
Quintana MG, Fernández MS, Salomón OD, 2012. Distribution and abundance of phlebotominae, vectors of leishmaniasis, in Argentina: spatial and temporal analysis at different scales. J Trop Med 2012:1-16.
Quintana MG, Salomón OD, De Grosso MSL, 2010. Distribution of phlebotomine sand flies (Dip-tera: Psychodidae) in a primary forest-crop interface, Salta, Argentina. J Med Entomol 47:1003-10.
Quintana MG, Santini MS, Cavia R, Martinez MF, Liotta DJ, Fernandez MS, Perez AA, Direni Mancini JM, Moya SL, Giuliani MG, Salomón OD (2020) Multiscale environmental determi-nants of Leishmania vectors in urban and rural context. Parasite Vector 13:502.
R Core Team, 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available from: https://www.R-project.org/
Salomón O, Sinagra A, Nevot M, Barberian G, Paulin P, Estevez J, Riarte A, Estevez J, 2008. First visceral leishmaniasis focus in Argentina. Mem I Oswaldo Cruz 103:109-11.
Salomón OD, 2019. Instructions on how to make an Outbreak of American Cutaneous Leishmaniasis. J Trop Med Health 03:18.
Salomón OD, Feliciangeli MD, Quintana MG, Afonso MM dos S, Rangel EF, 2015. Lutzomyia longipalpis urbanisation and control. Mem I Oswaldo Cruz 110:831-46.
Salomón OD, Mastrángelo AV, Santini MS, Liotta DJ, Yadon ZE, 2016. La eco-epidemiología retro-spectiva como herramienta aplicada a la vigilancia de la leishmaniasis en Misiones, Argentina, 1920-2014. Pan Am J Public Health 40:29-39.
Salomón OD, Orellano PW, Quintana MG, Perez S, Estani SS, Acardi S, Lamfri M, 2006a. Trans-misión de la Leishmaniasis Tegumentaria en la Argentina. Medicina (Buenos Aires) 66:211-9.
Salomón OD, Ramos LK, Quintana MG, Acardi SA, Santini MS, Schneider A, 2009. Distribución de vectores de Leishmaniasis Visceral en la provincia de Corrientes, 2008. Medicina (Buenos Aires) 69:625-30.
Salomón OD, Sosa-Estani S, Ramos K, Orellano PW, Sanguesa G, Fernández G, Sinagra A, Ra-pasciolli G, 2006b. Tegumentary leishmaniasis outbreak in Bella Vista City, Corrientes, Argenti-na during 2003. Mem I Oswaldo Cruz 101:767-74.
Sandoval Pacheco CM, Araujo Flores GV, Favero Ferreira A, Sosa Ochoa W, Ribeiro da Matta VL, Zúniga Valeriano C, Pereira Corbett CE, Dalastra Laurenti M, 2018. Histopathological features of skin lesions in patients affected by non-ulcerated or atypical cutaneous leishmaniasis in Hon-duras, Central America. Int Jf Exp Pathol 99:249-57.
Servicio Meteorológico Nacional, 2020. Estadísticas Climáticas. Ciudad de Corrientes. Available from: https://www.smn.gob.ar/estadisticas
Silveira TG, Teodoro U, Arraes SM, Lonardoni MV, Dias ML, Shaw JJ, Ishikawa EA, Lainson R, 1990. An autochthonous case of cutaneous leishmaniasis caused by Leishmania (Leishmania) amazonensis Lainson & Shaw, 1972 from the north of Paraná State, Brazil. Mem Inst Oswaldo Cruz 85:475-6.
Simpson, E, 1949. Measurement of diversity. Nature 163:688.
Talmoudi K, Bellali H, Ben-Alaya N, Saez M, Malouche D, Chahed MK, 2017. Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors. PLoS Neglect Trop D 11:e0005844.
Thomaz-Soccol V, Gonçalves AL, Piechnik CA, Baggio RA, Boeger WA, Buchman TL, Michaliszyn MS, Rodrigues dos Santos D, Celestino A, Aquino J, Leandro A de S, Paz OL de S da, Limont M, Bisetto A, Shaw JJ, Yadon ZE, Salomón OD, 2018. Hidden danger: Unexpected scenario in the vector-parasite dynamics of leishmaniases in the Brazil side of triple border (Argentina, Bra-zil and Paraguay). PLoS Neglect Trop D 12:e0006336.
Valero NNH, Uriarte M, 2020. Environmental and socioeconomic risk factors associated with visceral and cutaneous leishmaniasis: a systematic review. Parasitol Res 119:365-84.
VanDerWal J, Falconi L, Januchowski S, Shoo L, Storlie C, 2019. SDMTools - Species distribution modelling tools: tools for processing data associated with species distribution modelling exer-cises. Available from: https://CRAN.R-project.org/package=SDMTools
van Proosdij ASJ, Sosef MSM, Wieringa JJ, Raes N, 2016. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39:542-52.
Vignali S, Barras A, Braunisch V, 2020. SDMtune: species distribution model selection. Available from: https://github.com/ConsBiol-unibern/SDMtune
Wasserberg G, Abramsky Z, Kotler BP, Ostfeld RS, Yarom I, Warburg A, 2003. Anthropogenic dis-turbances enhance occurrence of cutaneous leishmaniasis in Israel deserts: patterns and mecha-nisms. Ecol Appl 13:868-81.

How to Cite

Andreo, V., Rosa, J., Ramos, K., & Salomón, O. D. (2022). Ecological characterization of a cutaneous leishmaniasis outbreak through remotely sensed land cover changes. Geospatial Health, 17(1). https://doi.org/10.4081/gh.2022.1033

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