PIPP Phase I: Next Generation Surveillance Incorporating Public Health, One Health, and Data Science to Detect Emerging Pathogens of Pandemic Potential

PIPP 第一阶段：结合公共卫生、单一健康和数据科学的下一代监测，以检测潜在大流行的新兴病原体

• 批准号: 2200299
• 负责人: David Ebert
• 金额: $ 100万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200299&HistoricalAwards=false
• 关键词: PIPP; Phase; Next; Generation; Surveillance

Emerging pathogens such as SARS-CoV-2 cross over from animals to humans and can cause new and deadly diseases. They spread before they are identified, allowing significant infection before detection and response. The threat of new diseases presents a Grand Challenge: How can we routinely collect and analyze data to provide early detection that can help prevent the spread of new diseases and stop the next pandemic? Delayed response to COVID-19 underscores the need for new early detection methods, more effective data management and integration, monitoring of the human-animal interface to detect new and emerging pathogens, and more cooperation and information sharing between animal and public health officials. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project will improve our ability to monitor and predict infectious disease threats using traditional and new data sources with novel computer algorithms to produce actionable information that will improve public-health responses to future pandemic threats. We will work with local and state public and animal health officials, practitioners, and community leaders to train them on the cutting-edge science while translating the results into solutions for metropolitan, rural and tribal nation communities. The outcome will be a comprehensive animal and public health surveillance, planning, and response roadmap that can be tailored to the unique needs of communities while enabling effective community response and management.This project will leverage multiple streams of information to identify signals of emerging threats. Achieving this goal requires the development of new diagnostic tools that provide novel information sources and computational frameworks that automate the process of ingesting, harmonizing, and analyzing large, dynamic, and heterogeneous data streams. This work develops and evaluates the outcomes of a set of techniques to surveil and identify the presence of and behavioral responses to an illness and/or pathogen in animals, communities, and individuals prior to symptom onset. The project leverages science-based, human-guided Artificial Intelligence (AI)/Machine Learning (ML) methods to analyze and fuse data streams from surveillance and environmental data to track predictive indicators across scales. These novel methods build on successful applications: wastewater surveillance to detect pathogens, pharmaceuticals, and human-health biomarkers indicative of community presence of existing or emerging infectious diseases (EIDs), animal surveillance to detect many EIDs, environmental modeling for forecasting infectious diseases, and breathomics to identify patients with lung cancer, COVID-19, and tuberculosis. This approach is novel in that it harnesses and integrates multiple surveillance data streams in a layered and parallel approach ensuring accuracy and specificity and enabling effective integration of individual-to-community-wide sampling scales into surveillance systems. This project enables effective design and evaluation of response planning techniques. 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.

新出现的病原体，如SARS-CoV-2，可以从动物传播到人类，并可能导致新的致命疾病。它们在被识别之前就传播了，在发现和反应之前就有了重大感染。新疾病的威胁提出了一个巨大的挑战：我们如何定期收集和分析数据，以提供早期发现，从而帮助防止新疾病的传播和阻止下一次大流行？对新冠肺炎的延迟反应突显出，需要新的早期检测方法，更有效的数据管理和集成，监测人-动物界面以检测新的和正在出现的病原体，以及动物和公共卫生官员之间更多的合作和信息共享。这一预防大流行的预测性情报(PIPP)第一阶段：发展赠款项目将提高我们监测和预测传染病威胁的能力，使用传统和新的数据源以及新的计算机算法来产生可操作的信息，以改进对未来大流行威胁的公共卫生反应。我们将与地方和州公共和动物卫生官员、从业者和社区领导人合作，在将成果转化为大都市、农村和部落社区的解决方案的同时，对他们进行尖端科学方面的培训。其成果将是一个全面的动物和公共卫生监测、规划和应对路线图，可以根据社区的独特需求进行量身定做，同时实现有效的社区响应和管理。该项目将利用多种信息流来识别新出现的威胁的信号。要实现这一目标，需要开发新的诊断工具，以提供新的信息源和计算框架，以自动化接收、协调和分析大型、动态和异类数据流的过程。这项工作开发和评估了一套技术的结果，以监测和识别疾病和/或病原体在症状出现之前在动物、社区和个人中的存在和行为反应。该项目利用以科学为基础、以人为本的人工智能(AI)/机器学习(ML)方法来分析和融合来自监测和环境数据的数据流，以跟踪不同规模的预测指标。这些新方法建立在成功应用的基础上：废水监测以检测病原体、药物和指示社区存在现有或新出现的传染病(EID)的人类健康生物标记物；动物监测以检测许多EID；环境建模用于预测传染病；以及呼吸组学用于识别肺癌、新冠肺炎和结核病患者。这种方法是新颖的，因为它以分层和并行的方法利用和整合了多个监测数据流，确保了准确性和特异性，并能够将从个人到社区范围的抽样尺度有效地整合到监测系统中。该项目能够有效地设计和评估应对规划技术。这一奖项由跨部门的大流行预防第一阶段预测情报(PIPP)计划支持，该计划由生物科学(BIO)、计算机信息科学和工程(CEISE)、工程(ENG)和社会、行为和经济科学(SBE)局长共同资助。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

Applying data science advances in disease surveillance and control

将数据科学进步应用于疾病监测和控制

• DOI: 10.56367/oag-039-10899
• 发表时间: 2023
• 期刊: Open Access Government
• 作者: Ebert, David S