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

精细尺度生态数据、高分辨率精确制图和区域网络建模的新颖整合，用于研究血吸虫病动态的环境驱动因素

• 批准号: 2011179
• 负责人: Giulio De Leo
• 金额: $ 245.28万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011179&HistoricalAwards=false
• 关键词: novel; integration; fine; scale; ecological

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.

在当今环境快速变化的时代，了解传染病的影响是科学和社会的优先事项。这个项目的目的是研究促进一种叫做血吸虫病或“蜗牛热”的令人衰弱的寄生虫病的环境因素。血吸虫病影响着全世界2亿多人，特别是在撒哈拉以南非洲地区。该项目团队最近开发的研究支持这样一种假设，即疾病传播率较高的地点靠近人们接触天然水源的地点，这些水源为寄生虫的宿主水生蜗牛提供了更多的栖息地。通过对当地水循环的研究，结合对当地疾病传播热点的测绘和野外环境采样，本研究将调查携带疾病的钉螺栖息地的季节性和年际变化如何影响人们的疾病风险。与低收入国家的公共卫生组织合作，团队成员将利用这一知识建立一种预测性测绘工具，衡量大片地区的血吸虫病风险。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。疾病风险测绘工具旨在支持公共卫生决策者更好地保护人类健康，方法是将救命的抗寄生虫药物有效地分发到最需要的地方，并采用精心设计的环境干预措施，降低环境从携带疾病的蜗牛向人传播的风险。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、本科生、研究生和博士后各级代表人数不足的群体的培训和专业发展。众所周知，血吸虫病的长期控制需要准确预测迅速变化的生态系统中淡水中间钉螺宿主的空间分布。然而，监控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，在最需要血吸虫病控制的低收入国家，抗击这种疾病的大规模项目通常不了解传播热点在哪里，以及哪些类型的干预措施最有效。该项目有三个目标：(1)通过微型水文建模和水生植被测绘，确定疾病传播的空间尺度；(2)开发新一代机器学习应用程序，利用无人机和卫星图像来确定对公共健康具有重要意义的蜗牛的主要栖息地范围，并最终确定区域范围内的传播热点；(3)将现场数据与栖息地的精确测绘相结合，以将血吸虫病动态的数学网络模型参数化，并利用最优控制理论确定疾病控制的成本效益战略的组合。该项目将提供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，媒介传播和水传播)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Pyrethroid insecticides pose greater risk than organophosphate insecticides to biocontrol agents for human schistosomiasis

• DOI: 10.1016/j.envpol.2022.120952
• 发表时间: 2023-01-10
• 期刊: ENVIRONMENTAL POLLUTION
• 影响因子: 8.9
• 作者: Haggerty，Christopher J. E.;Delius，Bryan K.;Rohr，Jason R.
• 通讯作者: Rohr，Jason R.

Schistosomiasis and climate change

• DOI: 10.1136/bmj.m4324
• 发表时间: 2020-11-16
• 期刊: The BMJ
• 作者: De Leo GA;Stensgaard AS;Sokolow SH;N’Goran EK;Chamberlin AJ;Yang GJ;Utzinger J
• 通讯作者: Utzinger J

Exposure, hazard, and vulnerability and their contribution to Schistosoma haematobium re-infection in northern Senegal

塞内加尔北部的暴露、危害和脆弱性及其对埃及血吸虫再次感染的影响

• DOI: 10.1016/s2542-5196(21)00094-2
• 发表时间: 2021
• 期刊: The Lancet Planetary Health
• 作者: Lund, Andrea J;Sokolow, Susanne H;Jones, Isabel J;Wood, Chelsea L;Ali, Sofia;Chamberlin, Andrew;Sy, Alioune Badara;Sam, M Moustapha;Jouanard, Nicolas;Schacht, Anne-Marie
• 通讯作者: Schacht, Anne-Marie

Agricultural Innovations to Reduce the Health Impacts of Dams

减少水坝对健康影响的农业创新

• DOI: 10.3390/su13041869
• 发表时间: 2021
• 期刊: Sustainability
• 影响因子: 3.9
• 作者: Lund, Andrea J.;Lopez-Carr, David;Sokolow, Susanne H.;Rohr, Jason R.;De Leo, Giulio A.
• 通讯作者: De Leo, Giulio A.

A planetary health innovation for disease, food and water challenges in Africa

针对非洲疾病、粮食和水挑战的全球健康创新

• DOI: 10.1038/s41586-023-06313-z
• 发表时间: 2023
• 期刊: Nature
• 影响因子: 64.8
• 作者: Rohr, Jason R.;Sack, Alexandra;Bakhoum, Sidy;Barrett, Christopher B.;Lopez-Carr, David;Chamberlin, Andrew J.;Civitello, David J.;Diatta, Cledor;Doruska, Molly J.;De Leo, Giulio A.
• 通讯作者: De Leo, Giulio A.