Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

Abstract

The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies

Uso e cobertura do solo e prevalência de leishmaniose visceral canina em Teresina, Piauí, Brasil: uma abordagem utilizando sensoriamento remoto orbital

“…However, their use in the study of VL is much more recent and relatively infrequent, particularly in Brazil, where VL transmission is zoonotic 33 . Consulting the MEDLINE (PubMed) and LILACS databases on 31/Dec/2015, only 12 studies were identified that used RS to assess the relationship between land use and land cover characteristics and spatial distribution of VL in the Brazilian context 16,18,19,29,34,35,36,37,38,39,40,41 .…”

“…Six studies on human VL found a direct association between vegetation cover and risk of VL 16,18,19,29,39,40 , while only one found an inverse association 41 . In the studies on vectors, two found higher vector density in areas with little vegetation cover 34,35 and one found a direct relationship between vegetation cover and presence of vectors 36 .…”

“…First, it is one of the few studies addressing zoonotic canine VL using satellite images, and is the one with the largest sample size. Second, except for a vector study that used IKO-NOS images 36 , the current study is the one that used images with the highest resolution (2.5 meters) in Brazil, which has been identified as an important characteristic to be considered in the study of VL, particularly in the urban setting 29,42 . Third, it uses OBIA, which combines spectral information with texture and context attributes, allowing greater flexibility for evaluating land use and land cover characteristics in the urban environment 16 .…”

Ecoepidemiology of American Visceral Leishmaniasis in Tocantins State, Brazil: Factors Associated with the Occurrence and Spreading of the Vector Lutzomyia (Lutzomyia) longipalpis (Lutz & Neiva, 1912) (Diptera: Psychodidae: Phlebotominae)

“…In the urban area of Campo Grande (MS), correlation was found between the abundance of L. (L.) longipalpis, percentage of vegetation cover, and the average vegetation index. However, there was no significant association between the diversity of habitats and abundance of the vector; the authors suggest that large trees can offer better microenvironmental conditions favoring the reproduction of the sand fly [71].…”

Sensoriamento Remoto Aplicado Ao Mapeamento E Quantificação De Áreas De Manguezal No Estado De Sergipe

“…Para este fim, foram adotadas ferramentas de sensoriamento remoto e geoprocessamento, as quais já demonstraram sua eficácia no monitoramento e mapeamento de endemias (CORREIA et al, 2004;OLIVEIRA et al, 2012) Estas amostras foram introduzidas em um banco de assinaturas do programa, e gerou-se um arquivo contendo a base de informações para a aplicação do algoritmo de busca na imagem. Com base nestes dados, foi realizada a classificação supervisionada da mesma, resultando em um conjunto de mapas temáticos aqui denominados pré-classificados.…”