Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa.
Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa.
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DOI：
10.1017/s0031182006001181
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发表时间：
2006-12
期刊：
影响因子：
2.4
通讯作者：
Brooker, S.
中科院分区：
文献类型：
--
作者：
Clements, A. C. A.;Moyeed, R.;Brooker, S.
关键词：
贝叶斯地统计模型被开发用于预测东非曼氏血吸虫感染的强度。从专门设计的标准化调查中获得了该区域459个地点31 458名学童（90%年龄在6-16岁之间）的流行病学数据，并与遥感环境数据结合使用，以确定与感染模式空间变化有关的因素。地统计学模型通过对数据拟合负二项分布明确考虑了寄生虫分布的高度聚集分布，并考虑了空间相关性。结果确定了环境风险因素在解释感染强度的地理异质性方面的作用，并显示了如何使用这些因素来开发预测图。这种地图对该区域的裂体线虫控制方案具有重要意义。
A Bayesian geostatistical model was developed to predict the intensity of infection with Schistosoma mansoni in East Africa. Epidemiological data from purposively-designed and standardized surveys were available for 31,458 schoolchildren (90% aged between 6-16 years) from 459 locations across the region and used in combination with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. The geostatistical model explicitly takes into account the highly aggregated distribution of parasite distribution by fitting a negative binomial distribution to the data and accounts for spatial correlation. Results identify the role of environmental risk factors in explaining geographical heterogeneity in infection intensity and show how these factors can be used to develop a predictive map. Such a map has important implications for schisosomiasis control programmes in the region.