Gomes Elainne Christine de Souza, Mesquitta Millena Carla da Silva, Wanderley Leandro Batista, de Melo Fábio Lopes, de Paula Souza E Guimarães Ricardo José, Barbosa Constança Simões
Laboratory and Reference Service in Schistosomiasis, Department of Parasitology, Aggeu Magalhães Institute, Fiocruz-Ministry of Health, Recife, Pernambuco, Brazil.
Geoprocessing Laboratory, Evandro Chagas Institute/SVS/MS, Brazil.
J Vector Borne Dis. 2018 Jul-Sep;55(3):208-214. doi: 10.4103/0972-9062.249142.
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.
: 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 Colitag™ 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.
: 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.
血吸虫病是一种农村地方性疾病,在巴西伯南布哥州已蔓延至城市和沿海地区。本研究旨在描述血吸虫病流行市镇中致病媒介生物——淡黄巴蜗牛的孳生地分布特征,并给出该媒介螺在新区域出现和扩散的预测模型。
2015年1月至12月在巴西伯南布哥州圣洛伦索达马塔市开展了一项贝类学调查,以确定淡黄巴蜗牛的孳生地。采用Colitag™诊断试剂盒测定粪便污染情况。收集降雨数据,并将其与蜗牛分布数据进行关联。借助空间分析软件包,运用核密度估计、克里金法和最大熵(MaxEnt)建模进行空间数据分析。
在划定的130个采集点中,64个被归类为淡黄巴蜗牛的孳生地。从这些地点共采集到5250只蜗牛。在这64个地点中,4个被视为血吸虫病传播疫点,54个为潜在传播疫点。观察到降雨与蜗牛密度呈负相关。核空间分析确定了三个蜗牛出现风险较高的区域,这些区域也是粪便污染最严重的区域,包括两个传播疫点。克里金法和MaxEnt建模模拟了通过核分析获得的情景。
使用地统计工具(克里金法和MaxEnt)对于识别风险区域以及估计淡黄巴蜗牛在研究区域内的扩散是有效的。研究区域内淡黄巴蜗牛的出现受雨季影响,因为在雨季刚结束后的时期它变得更为丰富,增加了扩散风险和新传播疫点出现的可能性。