Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.
Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.
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DOI：
10.1186/s12942-023-00331-w
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发表时间：
2023-06-02
影响因子：
4.9
通讯作者：
Carlton, Elizabeth J.
中科院分区：
文献类型：
--
作者：
Grover, Elise N.;Allshouse, William B.;Lund, Andrea J.;Liu, Yang;Paull, Sara H.;James, Katherine A.;Crooks, James L.;Carlton, Elizabeth J.
关键词：
虽然中间蜗牛的存在是当地血吸虫病传播发生的必要条件，但在接近消灭的地区将其作为监测目标是具有挑战性的，因为蜗牛宿主栖息地的斑块和动态质量使得收集和检测蜗牛劳动密集型。与此同时，依赖遥感数据的地理空间分析正在成为查明导致病原体出现和持续存在的环境条件的常用工具。在这项研究中，我们评估了开源环境数据是否可以用于预测家庭中人类血吸虫感染的存在，与使用全面蜗牛调查数据开发的预测模型相比，其准确度相似或更高。为此，我们使用2016年从中国西南部农村社区收集的感染数据来开发和比较两种随机森林机器学习模型的预测性能：一种使用蜗牛调查数据构建，另一种使用开源环境数据。环境数据模型在预测家庭S.日本血吸虫感染的估计精度和科恩的kappa值分别为0.89和0.49，在环境模型中，相比之下，准确性和kappa值为0.86和0.37的蜗牛模型。在我们的最终模型中，距离家半公里到一公里的水指数（地表水存在的指标）的归一化差异以及从家到最近道路的距离是表现最好的预测因素。如果住宅远离道路或靠近水道，则更有可能感染居民。我们的研究结果表明，在低传播环境中，利用开源环境数据可以比使用蜗牛调查更准确地识别人类感染的口袋。此外，从我们的模型变量的重要性措施指出，当地环境的方面，可能表明血吸虫病的风险增加。例如，如果家庭远离道路或被更多的地表水包围，则更有可能感染居民，这突出了未来监测和控制工作的目标区域。在线版本包含补充材料，可通过10.1186/s12942-023-00331-w获得。
Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen’s kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts. The online version contains supplementary material available at 10.1186/s12942-023-00331-w.
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影响因子：
2.4
作者：
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