The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-4/W2-2021
19 Aug 2021
 | 19 Aug 2021


C. Rodriguez Gonzalez, Y. Acevedo Arcia, and E. Frank Buss

Keywords: Landscape epidemiology, Leishmaniasis, Remote sensing, MaxEnt, Spatial models

Abstract. Leishmaniasis encompasses a group of vector-borne parasitic diseases, characterized by their diversity and complexity, that affect both humans and other vertebrates. They are caused by different species of parasites of the Leishmania genus, which are transmitted by bites from hematophagous female sandflies. This work proposed to model the occurrence probability of five sandflies species of sanitary interest for South America, from a bibliographic compilation of records of the last 10 years. To develop the model, the free software MaxEnt was used. This exploratory analysis made it possible to visualize the areas where the species are distributed. In addition, we analyzed land changes in vegetation around a town in Jujuy province, Argentina, where a leishmaniasis outbreak occurred during the years 2017 and 2018. For this, Sentinel-2 images were used, and a change vector was calculated for the difference between two dates of the Normalized Difference Vegetation Index (NDVI). This part of the work was made using SNAP software for images pre-procesing, Python for the change vector obtention and QGIS for the result post-procesing. From the exploration of MaxEnt software we were able to know the most suitable places for the distribution of the most important five species in the study region, and therefore, to project future decision-making to prevent and control leishmaniasis transmission. And in turn, obtain an approximation of how anthropogenic activities, as deforestation, can have an influence on leishmaniasis specific outbreaks transmitted by these species. Finally, from the exploration of the different tools used in this work, the importance of validation with field data for the generation of accurate analyses and predictions is highlighted. It implies that more data collection is necessary to validate the models and analyzes generated, to guarantee the contribution of the tools in macro-ecological studies of species linked to disease transmission.