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

新冠肺炎大流行表明，我们国家迫切需要一个能够迅速适应或借鉴预期或意外的公共卫生危机的下一代公共卫生体系。通过利用人工智能(AI)的最新进展，这样的系统应该能够预测、检测和响应快速演变的紧急公共卫生危机，并以可持续和可扩展的方式迅速恢复其先前的性能水平。为了实现这一目标，该项目旨在解决建立在隐私和包容性的基础上的全国性数字基础设施的社会技术设计的重大挑战，以预测和预防流行病。一个由来自多个机构的多学科研究人员组成的团队将领导一系列基础和综合研究项目，这些项目既包括微观层面的粒度数据，也包括人口层面的数据，以从不同方面应对这一重大挑战。我们精心计划了一系列活动，包括会议、研讨会和研讨会，以创建一个有效的研究团队，让不同和包容的利益相关者(例如，公共卫生部门、医疗保健/医院系统、工业/私营部门以及地理和种族不同的社区利益相关者)参与进来，并教育和培训下一代研究人员进行团队科学。为了开发同时保护隐私的数字、自主和分布式基础设施，该团队将专注于数据存储和收集的体系结构，以及数据共享的隐私使能器。数据收集基础设施和隐私赋能技术将(I)小心地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；以及(Iii)允许个人(数据捐赠者)完全控制其数据并给予知情同意，同时以不同的方式与不同的数据收集者(研究人员)共享他们的数据。此外，该团队将开发一套高度整合的研究项目，协调和智能地为大流行预防工作，同时扩大参与和包容。这些项目包括(I)使用可穿戴设备结合人口水平的社会、经济、文化和环境指标进行早期检测；(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；(Iii)多层次和多方面的监测；以及(Iv)基于药物重新定位的新疾病的技术准备。这两个目标相辅相成，协同工作，以实现个人和人口层面的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性。该奖项得到跨部门大流行预防第一阶段预测情报(PIPP)计划的支持，该计划由生物科学(BIO)、计算机信息科学和工程(CESE)、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。

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.