PIPP Phase I: Comprehensive, Integrated, Intelligent System for Early and Accurate Pandemic Prediction, Prevention, and Preparation at Personal and Population Levels

COVID-19大流行表明，我国迫切需要能够快速适应并从预期或未预期的公共卫生危机中吸取教训的下一代公共卫生系统。通过利用人工智能（AI）的最新进展，该系统应能够预测、检测和应对快速演变的突发公共卫生危机，并以可持续和可扩展的方式快速恢复其先前的性能水平。为了实现这一目标，本项目旨在解决建立在隐私和包容性基础上的用于流行病预测和预防的全国性数字基础设施的社会技术设计的巨大挑战。来自多个机构的多学科研究团队将领导广泛的基础和综合研究项目，包括微观层面的颗粒数据和人口层面的数据，以应对来自不同方面的重大挑战。精心策划了一系列活动，包括会议、讲习班和研讨会，以创建一个有效的研究团队，吸引多样化和包容性的利益相关者（例如，公共卫生部门、卫生保健/医院系统、工业/私营部门以及地理和种族多样化的社区利益相关者），并教育和培训下一代研究人员进行团队科学。为了开发一个保护隐私的数字、自治和分布式基础设施，该团队将专注于数据存储和收集的架构，以及数据共享的隐私使能器。数据收集基础设施和隐私启用技术将(i)仔细平衡数据效用和隐私；（ii）平衡已知隐私风险的脆弱性和保护敏感数据的机构需求；（iii）允许个人（数据提供者）完全控制其数据，并在以不同方式与不同的数据收集者（研究人员）共享其数据时给予知情同意。此外，该小组将制定一套高度综合的研究项目，以协调和明智地开展大流行病预防工作，同时扩大参与和包容。这些项目包括：(i)结合人口水平的社会、经济、文化和环境指标，使用可穿戴设备进行早期检测；（ii）病原体传播的数学建模、基于时空分析的热点预测和缓解措施；（三）多层次、多方位的监控；（四）基于药物重新定位的新疾病技术准备。这两个目标相辅相成，协同工作，以实现在个人和人群层面上进行早期和准确的大流行预测、预防和准备的最终目标，同时也将确保隐私和包容。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）委员会共同资助。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。

The COVID-19 pandemic demonstrates that our country desperately needs a next generation public health system that can quickly adapt to, and learn from an expected or not-expected public health crisis. By taking advantage of recent advances in artificial intelligence (AI), such a system shall be able to predict, detect, and respond to rapidly evolving emergent public health crises, and resume its prior performance level rapidly in a sustainable and scalable way. Towards that goal, this project aims to tackle the grand challenge of sociotechnical design of nation-wide digital infrastructure for pandemic prediction and prevention, which is built on the foundation of privacy and inclusiveness. A multi-disciplinary team of researchers from multiple institutions will lead a broad range of fundamental and integrated research projects that incorporate both micro-level granular data and population-level data to tackle the grand challenge from different aspects. A set of activities, including meetings, workshops, and seminars, have been carefully planned to create an effective research team, to engage diverse and inclusive stakeholders (e.g., public health departments, health care/hospital systems, industrial/private sectors, and geographically and ethnically diverse community stakeholders), and to educate and train next generation researchers to conduct team science.In order to develop a digital, autonomous, and distributed infrastructure that is also privacy preserving, the team will focus on the architecture for data storage and collection, as well as privacy enablers for data sharing. The data collection infrastructure and privacy enabler technologies will (i) carefully balance data utility and privacy; (ii) balance vulnerability for known privacy risks and institutional needs to protect sensitive data; and (iii) allow individuals (data donors) to have full control over their data and to give informed consent while sharing their data in different ways with different data collectors (researchers). In addition, the team will develop a set of highly integrated research projects that work coordinately and intelligently for pandemic prevention that also broaden participation and inclusion. The projects include (i) early detection using wearable devices in combination with population level social, economic, cultural and environmental indicators; (ii) mathematical modeling of pathogen transmission, hotspot prediction based on spatio-temporal analysis, and mitigation; (iii) multi-level and multi-faceted surveillance; and (iv) technological preparation for new diseases based on drug repositioning. The two aims are complementary to each other and work synergistically to achieve the ultimate goal of early and accurate pandemic prediction, prevention, and preparation at personal and population levels that will also ensure privacy and inclusion.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.