A novel integration of fine scale ecological data, high-resolution precision mapping, and regional network modeling to investigate environmental drivers of schistosomiasis dynamics

在当今环境迅速变化的时代，了解传染病的影响是科学和社会的优先事项。该项目的目的是研究促进一种叫做血吸虫病或“蜗牛热”的使人衰弱的寄生虫病的环境因素。血吸虫病影响全世界2亿多人，特别是在撒哈拉以南非洲。该项目小组最近开展的研究支持了这样一种假设，即在人们接触天然水源的地方，疾病传播的可能性更高，这些地方为作为寄生虫宿主的水生蜗牛提供了更多的栖息地。本研究将通过对当地水循环的研究，结合当地疾病传播热点的制图和野外环境采样，探讨携带疾病的蜗牛栖息地的季节性和年际变化对人类疾病风险的影响。小组成员将与低收入国家的公共卫生组织合作，利用这些知识建立一种预测测绘工具，在大范围内测量血吸虫病风险。该团队将采用新技术，如卫星和无人机图像以及人工智能来进行预测。疾病风险绘图工具旨在支持公共卫生决策者更好地保护人类健康，方法是在最需要的地方有效分发挽救生命的抗寄生虫药物，并采用精心设计的环境干预措施，减少携带疾病的蜗牛在环境中传播给人的风险。该项目还将通过直接参与研究、强化课程和国际讲习班，支持在高中、本科、研究生和博士后各级的代表性不足群体的培训和专业发展。众所周知，长期控制血吸虫病需要准确预测淡水中间蜗牛宿主在快速变化的生态系统中的空间分布。然而，监测这些中间主机的标准技术既费力又耗时，而且提供的信息仅限于手动采样的小区域。因此，在最需要控制血吸虫病的低收入国家，开展大规模防治这一疾病的规划时，通常对传播热点在哪里以及哪种干预措施最有效知之甚少。该项目有三个目标：(1)通过微尺度水文建模和水生植被制图确定疾病传播发生的空间尺度；(2)开发新一代机器学习应用程序，利用无人机和卫星图像识别具有公共卫生重要性的蜗牛的主要栖息地范围，并最终确定区域尺度上的传播热点；(3)将野外数据与血吸虫栖息地的精确测绘相结合，参数化血吸虫病动态的数学网络模型，并利用最优控制理论确定具有成本效益的疾病控制策略组合。该项目将提供一个基于生态学理论的研究框架和流行病学模型，以预测疾病动态，可用于应对具有复杂生态和环境驱动因素的其他疾病（例如病媒传播和水媒传播）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In today’s era of rapid environmental change, understanding the implications for infectious disease is a priority for both science and society. The aim of this project is to study environmental factors that promote a debilitating parasitic disease called schistosomiasis, or “snail fever.” Schistosomiasis affects more than 200 million people worldwide, especially in sub-Saharan Africa. Recent research developed by this project team supports the hypothesis that disease transmission is higher near sites where people come into contact with natural water sources that have more habitat available for the aquatic snails that are hosts of the parasite. By studying local water circulation, combined with mapping and field environmental sampling at local disease transmission hotspots, this research will investigate how seasonal and year-to-year change in the habitat of disease-carrying snails affects disease risk for people. In collaboration with public health organizations in low-income countries, team members will use this knowledge to build a predictive mapping tool that measures schistosomiasis risk across large landscapes. The team will employ new technologies, such as satellite and drone imagery and artificial intelligence to make predictions. The disease risk mapping tool is intended to support public health decision-makers to better protect human health by efficiently distributing life-saving anti-parasitic medicine where it is needed most, and by employing well-designed environmental interventions that reduce the risk of environmental transmission from disease-carrying snails to people. This project will also support the training and professional development of underrepresented groups at the high school, undergraduate, graduate and postdoctoral levels, through direct involvement in research, intensive courses and international workshops.It is well known that long-term control of schistosomiasis requires accurate prediction of the spatial distribution of freshwater intermediate snail hosts in rapidly changing ecosystems. Yet, standard techniques for monitoring these intermediate hosts are labor-intensive and time-consuming, and provide information limited to the small areas that are manually sampled. Consequently, in the low-income countries where schistosomiasis control is most needed, large-scale programs to fight this disease generally operate with little understanding of where transmission hotspots are, and what types of intervention are most effective. This project has three objectives: (1) to determine the spatial scale at which disease transmission occurs through microscale hydrological modelling and mapping of aquatic vegetation; (2) to develop a new generation of machine learning applications that use drone and satellite imagery to identify key habitat extent for snails of public health importance, and, ultimately, transmission hotspots at regional scales; and, (3) to integrate field data with precision mapping of habitat to parameterize mathematical network models of schistosomiasis dynamics, and use Optimal Control theory to identify combinations of cost-effective strategies for disease control. This project will provide a research framework and epidemiological models based on ecological theory to predict disease dynamics that can be used to respond to other diseases (e.g., vector-borne and water borne) with complex ecologies and environmental drivers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.