PIPP Phase I: Advancing Environmental Surveillance for Pandemic Prediction in Remote and Resource Poor Settings

具有大流行潜力的病原体，如埃博拉病毒、艾滋病毒和禽流感，经常出现在资源匮乏或卫生保健基础设施有限的偏远地区。不幸的是，经济、后勤、行为和技术障碍限制了在这些环境中进行全面的临床试验。因此，在确定对公共卫生的威胁之前，疾病可能不受控制地传播。环境监测已成为监测和跟踪传染病传播和传播的现有临床工具的一个多功能和补充平台。环境监测的基本前提是，从空气、土壤、水和废水样品中提取微生物和病毒生物标志物的检测和定量，可用于监测传染病的发病、传播和社区传播。虽然环境监测战略可以克服与全面临床检测相关的一些成本和后勤障碍，但在资源匮乏的环境中，与环境采样和数据分析相关的技术障碍以及在正确的时间将结果传达给正确的人的挑战限制了其在可能发生新发病原体的“热点”环境中的实施。预防大流行预测情报（PIPP）第一阶段：发展赠款项目的总体目标是应对与制定和部署具有大流行潜力的病原体的“普遍”环境监测战略相关的挑战。为了推进这一目标，项目团队将利用PIPP一期规划拨款为建立一个拥有全球环境监测工具网络的中心奠定基础，该中心将部署在新出现的传染病热点地区，以检测关注的病原体，并及时通知地方、区域和国家卫生系统领导人和利益相关者。该中心的活动将包括有针对性的研究项目、研讨会和劳动力发展，包括培训和指导肯塔基大学和亚利桑那州立大学的两名博士后研究员和一名研究生。该项目将建立新发环境病原体发现中心（C-DEEP），其任务是推进环境监测和宏基因组学科学，特别是在可能出现大流行病的资源匮乏或偏远地区以及目前疾病监测程序不足的地区。项目团队与撒哈拉以南非洲和东南亚的长期合作伙伴合作，计划通过建立跨学科合作，严格界定知识和技术差距，以及开展旨在丰富C-DEEP能力的初步研究，扩大新发病原体环境监测方面的现有专业知识。该项目的具体目标是：1)确定获取和分析环境样本的设置特定障碍；2)有效地将“大数据”读数（如宏基因组数据）转化为可操作的公共卫生政策变化；3)建立和发展致力于应对这一挑战的利益相关者的多元化联盟；4)进行有针对性的试点实验，以证明概念的可行性和降低关键分析过程的风险。开发更有效的环境监测工具有可能识别新出现的病原体，支持对季节性流感和SARS-CoV-2等已存在和散发病原体的监测，并监测抗微生物药物耐药性标志物。通过国际伙伴关系开发和部署环境宏基因组学技术和工具，还可以加强地方疾病监测能力，并为资源匮乏和偏远地区的大流行病防范建立全球网络，从而能够在往往最需要监测的社区进行监测。该奖项由流行病预防的跨部门预测情报第一阶段（PIPP）计划提供支持，该计划由生物科学（BIO）、计算机信息科学与工程（CISE）、工程（ENG）和社会、行为和经济科学（SBE）委员会联合资助。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。

Pathogens with pandemic potential such as Ebola, HIV, and avian influenza frequently emerge in low-resources or remote settings with limited healthcare infrastructure. Unfortunately, economic, logistical, behavioral, and technological barriers limit comprehensive clinical testing in these settings. Thus, disease may spread unchecked before a threat to public health is identified. Environmental surveillance has emerged as a versatile and complementary platform to existing clinical tools for monitoring and tracking the spread and transmission of infectious diseases. The basic premise of environmental surveillance is that the detection and quantification of microbial and virial biomarkers extracted from air, soil, water, and wastewater samples can be used to monitor the onset, spread and community transmission of infectious diseases. While environmental surveillance strategies can overcome some of the cost and logistical barriers associated with comprehensive clinical testing, technological hurdles related to environmental sampling and data analysis in low-resource settings and challenges in communicating results to the right people at the right time have limited its implementation in “hotspot” settings where emerging pathogens are likely to occur. The overarching goal of this Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project is to address the challenges associated with the development and deployment of “universal” environmental surveillance strategies for pathogens of pandemic potential. To advance this goal, the project team will leverage funding for this PIPP Phase I planning grant to lay the foundation for the establishment of a Center with globally linked network of environmental surveillance tools that would be deployed in emerging infectious disease hot spots to detect pathogens of concern with timely notification of local, regional, and national health system leaders and stakeholders. The Center activities will include targeted research projects, workshops, and workforce development including the training and mentoring of two post-doctoral fellows and a graduate student at the University of Kentucky and Arizona State University.This project will establish the Center for the Discovery of Emerging Environmental Pathogens (C-DEEP) with a mission to advance the science of environmental surveillance and metagenomics, especially in low-resources or remote settings where pandemics are likely to emerge and where current disease surveillance processes are inadequate. In collaboration with longstanding partners in Sub-Saharan Africa and Southeast Asia, the project team plans to extend existing expertise in environmental surveillance of emerging pathogens by building transdisciplinary collaborations, critically defining knowledge and technology gaps, and conducting preliminary research designed to enrich the capabilities of the C-DEEP. The specific objectives of the project are to: 1) Identify setting-specific obstacles for obtaining and analyzing environmental samples; 2) Efficiently translate “big data” readouts (e.g., metagenomic data) into actionable public health policy changes; 3) Build and grow a diverse alliance of stakeholders who are committed to this challenge; and 4) Conduct targeted pilot experiments to demonstrate concept feasibility and de-risk critical analytic processes. The development of more effective environmental surveillance tools has the potential to identify emerging pathogens, support surveillance of established and sporadic pathogens, such as seasonal influenza and SARS-CoV-2 and monitor antimicrobial resistance markers. The development and deployment of environmental metagenomics technologies and tools through international partnerships could also bolster local disease surveillance capacity and create global networks for pandemic preparedness in low-resource and remote settings thereby enabling surveillance in communities where it is often most needed. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).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.