Zhang Xuedong, Lv Zelan, Dai Jianjun, Ke Yongwen, Chen Xinyue, Hu Yi
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, 102627, China.
Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, 100038, China.
Heliyon. 2024 Aug 13;10(16):e36300. doi: 10.1016/j.heliyon.2024.e36300. eCollection 2024 Aug 30.
Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due to the widespread distribution of , the sole intermediate host of . This study aims to address the constraints of existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt a multi-source data-driven Random Forest approach that integrates bottomland and ground-surface texture data with traditional environmental variables, enhancing the accuracy of snail habitat assessments. We developed four distinct models for the lake and marshland areas of Guichi, China: a baseline model incorporating ground-surface texture, bottomland variables, and environmental variables; Model 1 with only environmental variables; Model 2 adding ground-surface texture and environmental variables; and Model 3 integrating bottomland with environmental variables. The baseline model outperformed the others, achieving a true skill statistic of 0.93, an accuracy of 0.97, a kappa statistic of 0.94, and an area under the curve of 0.99. Our analysis pinpointed critical high-risk snail habitats distributed in a belt-like pattern along major water bodies, near the Yangtze River, QiuPu River, and around Shengjin Lake, Jiuhua River, and Qingtong River. These insights can aid local health authorities in more efficiently allocating limited resources, developing effective snail surveillance and control strategies to combat schistosomiasis. Additionally, this approach can be adapted to localize other endemic hosts with similar ecological characteristics.
日本血吸虫病在中国仍然是一个重大的公共卫生挑战，主要原因是其唯一中间宿主钉螺广泛分布。本研究旨在解决现有遥感分析在识别钉螺栖息地方面的局限性，这些分析常常忽视空间尺度和季节变化。为此，我们采用了一种多源数据驱动的随机森林方法，该方法将滩地和地表纹理数据与传统环境变量相结合，提高了钉螺栖息地评估的准确性。我们为中国贵池的湖泊和沼泽地区开发了四个不同的模型：一个包含地表纹理、滩地变量和环境变量的基线模型；仅包含环境变量的模型1；添加地表纹理和环境变量的模型2；以及将滩地与环境变量相结合的模型3。基线模型的表现优于其他模型，真技能统计量为0.93，准确率为0.97，kappa统计量为0.94，曲线下面积为0.99。我们的分析确定了关键的高风险钉螺栖息地，它们沿着主要水体呈带状分布，靠近长江、秋浦河以及升金湖、九华河和青通河周边。这些见解有助于当地卫生当局更有效地分配有限资源，制定有效的钉螺监测和控制策略以抗击血吸虫病。此外，这种方法可进行调整，以定位具有类似生态特征的其他地方性宿主。
