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Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa.

机译：非洲血吸虫病流行病学和生态学的遥感，地理信息系统和空间分析。

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摘要

Beginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.
机译：从1970年开始，遥感（RS）技术与地理信息系统（GIS）相结合的潜力不断提高，以增进我们对非洲血吸虫病的流行病学和控制的了解。在我们当前的综述中，给出了RS，GIS和空间分析的工作定义，并总结了迄今为止使用RS和GIS在非洲血吸虫病的流行病学和生态学中的应用。在确定人类感染的流行程度和中间宿主蜗牛的分布方面已取得进展。最近，贝叶斯地统计建模方法已被用于预测不同规模感染的发生率和强度。但是，仍然存在许多挑战。因此需要新的研究来克服这些限制。首先，更大的空间和时间分辨率对于改善风险图谱和了解地方尺度上的传播动态似乎很重要。第二，通过考虑人们的社会经济状况信息，可以实现更现实的风险分析;此外，未来的努力应纳入有关家庭获得清洁水和适当卫生设施以及行为和教育问题的数据。第三，有关中间宿主蜗牛分布的高质量数据应有助于验证感染风险图和对传播动态进行建模。最后，应更加注重风险图谱和多种物种寄生虫感染的预测，以整合疾病风险图谱并提高其控制的成本效益。

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