Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models
Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models
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DOI：
10.1016/j.actatropica.2013.12.007
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发表时间：
2014-04-01
期刊：
影响因子：
2.7
通讯作者：
Vounatsou, Penelope
中科院分区：
文献类型：
--
作者：
Scholte, Ronaldo G. C.;Gosoniu, Laura;Vounatsou, Penelope
血吸虫病是包括巴西在内的热带和亚热带地区最常见的寄生虫病之一。巴西在1970年代中期启动了一项国家控制方案，事实证明，该方案在控制发病率方面取得了成功，因为涉及肝脾的病例数量大大减少。为了巩固控制并走向消灭，需要关于血吸虫病空间分布的可靠地图，以便干预措施能够针对风险最高的社区。这项研究的目的是绘制曼氏血吸虫在巴西的分布图。我们利用了2005-2009年国家血吸虫病控制方案中现成的流行数据，得出了遥感气候和环境数据，并从各种来源获得了社会经济数据。将数据整理成地理信息系统，并开发了贝叶斯地质统计模型。基于模型的地图确定了与曼氏葡萄球菌传播有关的重要风险因素，并确认环境变量与贫困指数密切相关。我们的平滑预测风险图，包括不确定性，突出了干预的优先地区，即北部和东南部地区的北部以及东北地区的东部。我们的预测风险图为加强现有的监测-反应机制提供了一个有用的工具。(C)爱思唯尔出版的2014年。
Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms. (C) 2014 Published by Elsevier B.V.