Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.
Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.
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DOI：
10.1371/journal.pntd.0006517
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发表时间：
2018-06
影响因子：
3.8
通讯作者：
Naumova EN
中科院分区：
文献类型：
--
作者：
Kulinkina AV;Walz Y;Koch M;Biritwum NK;Utzinger J;Naumova EN
血吸虫病是一种与水有关的被忽视的热带疾病。在许多流行的低收入和中等收入国家，监测和报告不足导致对血吸虫病病例的人口和地理分布特征描述不清。因此，建模依赖于预测高传输区域并告知控制策略。我们假设利用遥感（RS）环境数据结合水、环境卫生和个人卫生（WASH）变量可以改进当前的预测建模方法。利用从73所加纳农村学校收集的血血吸虫流行数据，建立了一个随机森林模型，研究了精确空间分辨率（10-30 m）的RS数据（Landsat 8、Sentinel-2和全球数字高程模型）得出的15个环境变量的预测能力。试验了五种变量提取方法，以确定学校患病率与潜在传播地点的环境条件之间的空间联系，包括将模型应用于已知的人类水接触地点。最后，将当地取水途径和地下水质量的措施纳入基于rs的模型，以评估环境和WASH变量的相对重要性。与基于测量流行率地点的环境特征的传统方法相比，基于人们接触地表水的特定地点的环境特征的预测模型提供了一些改进。水指数（MNDWI）和地形变量（高程和坡度）是重要的环境风险因素，而在包含WASH变量的组合模型中，地下水铁浓度占主导地位。该研究有助于了解血吸虫病传播的局部驱动因素。具体而言，钻孔水质不理想使人们长期依赖地表水，间接增加了血吸虫病的风险，并导致快速再感染（预防性化疗后6个月患病率高达40%）。在血吸虫病预测中考虑与wash相关的危险因素有助于将控制策略的重点从治疗症状转移到减少接触。血吸虫病是一种与水有关的被忽视的热带病，对低收入和中等收入国家贫困社区的学龄儿童的影响尤为严重。血吸虫病传播风险受环境、社会经济和行为因素的影响，包括水、环境卫生和个人卫生（WASH）条件。我们使用精细空间分辨率（10-30米）遥感数据，结合当地水资源获取和地下水质量测量，预测了加纳73个农村社区的血吸虫病风险。我们发现，将环境模型应用于人们接触地表水体的特定地点（即潜在传播地点），而不是应用于测量流行率的地点，可以改善模型的性能。遥感水指数和地形变量（高程和坡度）是重要的环境风险因子，但总体而言，地下水铁浓度占主导地位。在研究地区，钻孔水质不理想使人们长期依赖地表水，间接增加了血吸虫病的风险，并导致快速再感染（驱虫后6个月患病率高达40%）。在血吸虫病预测中考虑与wash相关的危险因素有助于将控制策略的重点从治疗症状转移到减少接触。
Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10–30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure. Schistosomiasis is a water-related neglected tropical disease that disproportionately affects school-aged children in poor communities of low- and middle-income countries. Schistosomiasis transmission risk is affected by environmental, socioeconomic, and behavioral factors, including water, sanitation, and hygiene (WASH) conditions. We used fine spatial resolution (10–30 m) remotely sensed data, in combination with measures of local water access and groundwater quality, to predict schistosomiasis risk in 73 rural Ghanaian communities. We found that applying environmental models to specific locations where people contact surface water bodies (i.e., potential transmission locations), rather than to locations where prevalence is measured, improved model performance. A remotely sensed water index and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated. In the study area, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following deworming). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.
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影响因子：
3.8
作者：
Hotez PJ;Alvarado M;Basáñez MG;Bolliger I;Bourne R;Boussinesq M;Brooker SJ;Brown AS;Buckle G;Budke CM;Carabin H;Coffeng LE;Fèvre EM;Fürst T;Halasa YA;Jasrasaria R;Johns NE;Keiser J;King CH;Lozano R;Murdoch ME;O'Hanlon S;Pion SD;Pullan RL;Ramaiah KD;Roberts T;Shepard DS;Smith JL;Stolk WA;Undurraga EA;Utzinger J;Wang M;Murray CJ;Naghavi M
通讯作者：
Naghavi M
DOI：
10.4269/ajtmh.2007.76.956
发表时间：
2007-05-01
影响因子：
3.3
作者：
Beck-Woerner, Christian;Raso, Giovanna;Utzinger, Juerg
通讯作者：
Utzinger, Juerg
影响因子：
5.1
作者：
Kampichler, Christian;Wieland, Ralf;Arriaga-Weiss, Stefan
通讯作者：
Arriaga-Weiss, Stefan
影响因子：
2.7
作者：
Kosinski, Karen C.;Bosompem, Kwabena M.;Gute, David M.
通讯作者：
Gute, David M.
影响因子：
9.6
作者：
Brooker S;Hay SI;Bundy DA
通讯作者：
Bundy DA