Remote Sensing Postgraduate Program (PGSER), Coordination of Teaching, Research and Extension (COEPE), National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil.
Earth Observation and Geoinformatics Division (DIOTG), General Coordination of Earth Science (CG-CT), National Institute for Space Research (INPE), São José dos Campos, Brazil.
PLoS Negl Trop Dis. 2024 Nov 4;18(11):e0012582. doi: 10.1371/journal.pntd.0012582. eCollection 2024 Nov.
Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas.
METHODOLOGY/PRINCIPAL FINDINGS: In the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed.
CONCLUSIONS/SIGNIFICANCE: This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.
血吸虫病是一种慢性寄生虫病,在热带和亚热带地区仍然是一个公共卫生问题,特别是在缺乏安全用水和适当卫生设施的中低收入国家。巴西卫生部于 2011 年至 2015 年进行的一项全国流行情况调查发现,人类感染率下降到 1%,26 个州中有 19 个仍被列为流行地区。在低流行地区,血吸虫病有重新成为公共卫生关注的风险。本研究提出了一种基于景观的综合方法,以帮助监测和控制低流行地区的血吸虫病。
方法/主要发现:在中帕拉纳潘巴河流域,使用综合方法确定了与血吸虫病相关的特定景观。该方法融合了遥感、环境、社会经济、流行病学和贝类学数据。一个专家组确定了与不同程度血吸虫病传播潜力相关的十个不同的景观类别。这些类别被用于训练监督分类机器学习算法,得到了 92.5%的总体准确性和 6.5%的分类错误率。评估结果显示,在流域内五个关键城市的水采集样本中,有 74.6%的采集蜗牛属于具有更高曼氏血吸虫感染潜力的景观类型。还分析了景观连通性指标。
结论/意义:本研究强调了基于综合景观分析在制定消除血吸虫病策略方面的作用。该方法生成了涵盖整个流域的新的血吸虫病风险地图。该地区的低流行率部分可以解释为更易引发血吸虫病传播的分组景观单元之间的连通性有限。然而,社会、经济和环境景观的变化,特别是与该地区不完全城市化进程加速相关的变化,有可能增加血吸虫病传播的风险。本研究将有助于将干预措施集中在使该地区更接近消除血吸虫病的目标上。