Spatial risk analysis on occurrences and dispersal of Biomphalaria straminea in and endemic area for schistosomiasis

• Autor(a) principal: Gomes, Elaine Christine de Souza
• Data de Publicação: 2018
• Outros Autores: Mesquitta, Millena Carla da Silva, Wanderley, Leandro Batista, Melo, Fábio Lopes de, Guimarães, Ricardo José de Paula Souza e, Barbosa, Constança Simões
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Digital do Instituto Evandro Chagas (Patuá)
• Texto Completo: https://patua.iec.gov.br/handle/iec/3630
• topic: Biomphalaria / parasitologia; Esquistossomose / transmissão; Esquistossomose / epidemiologia; Doenças endêmicas; Criação de animais; Vetores de Doença; Fezes / parasitologia; Análise espacial; Sistemas de Informação Geográfica; São Lourenço da Mata (PE)

Background & objectives: Schistosomiasis is a rural endemic disease that has been expanding to urban and coastal areas in the state of Pernambuco, Brazil. The aim of this study was to characterize the distribution of breeding sites of the causative vector, Biomphalaria straminea in an endemic municipality for schistosomiasis and to present the predictive models for occurrences and dispersal of this vector snail to new areas. Methods: A malacological survey was conducted during January to December 2015 in the municipality of São Lourenço da Mata, Pernambuco, Brazil to identify the breeding sites of Biomphalaria. Faecal contamination was determined by means of the ColitagTM diagnostic kit. Rainfall data were collected, and correlated with snail distribution data. Kernel density estimation, kriging and maximum entropy (MaxEnt) modeling were used for spatial data analysis, by means of the spatial analysis software packages. Results: Out of the 130 demarcated collection points, 64 were classified as breeding sites for B. straminea. A total of 5,250 snails were collected from these sites. Among these 64 sites, four were considered as foci of schistosomiasis transmission and 54 as potential transmission foci. An inverse relationship between rainfall and snail density was observed. Kernel spatial analysis identified three areas at higher risk of snail occurrence, which were also the areas of highest faecal contamination and included two transmission foci. Kriging and MaxEnt modeling simulated the scenarios obtained through the kernel analyses. Interpretation & conclusion: Use of geostatistical tools (Kriging and MaxEnt) is efficient for identifying areas at risk and for estimating the dispersal of Biomphalaria species across the study area. Occurrence of B. straminea in the study area is influenced by the rainy season, as it becomes more abundant during the period immediately after the rainy season, increasing the risk of dispersal and the appearance of new transmission foci.