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

PIPP第一阶段：全面、集成、智能的系统，用于个人和人群层面的早期、准确的流行病预测、预防和准备

基本信息

批准号：
2200255
负责人：
Jing Li
金额：
$ 100万
依托单位：
Case Western Reserve University
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：
2022-07-15 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200255&HistoricalAwards=false
关键词：
PIPP Phase Comprehensive Integrated Intelligent

项目摘要

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.

COVID-19 大流行表明，我国迫切需要下一代公共卫生系统，能够快速适应预期或意外的公共卫生危机并从中吸取教训。通过利用人工智能（AI）的最新进展，这样的系统应能够预测、检测和响应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，该项目旨在应对建立在隐私和包容性基础上的用于大流行预测和预防的全国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，结合微观层面的颗粒数据和人口层面的数据，从不同方面应对这一巨大挑战。精心策划了一系列活动，包括会议、讲习班和研讨会，以创建有效的研究团队，吸引多元化和包容性的利益相关者（例如公共卫生部门、医疗保健/医院系统、工业/私营部门和地理区域）。和种族多元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。为了开发一个数字化、自治和分布式的基础设施，同时保护隐私，该团队将专注于数据存储和收集的架构，以及隐私推动者数据共享。数据收集基础设施和隐私支持技术将（i）仔细平衡数据实用性和隐私； (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）共享其数据。此外，该团队还将开发一套高度综合的研究项目，协调、智能地开展疫情预防工作，同时扩大参与和包容性。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) 病原体传播的数学模型、基于时空分析的热点预测和缓解措施； (iii) 多层次、多方面的监督；（四）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，协同作用，以实现在个人和人口层面及早、准确地预测、预防和准备大流行病的最终目标，同时确保隐私和包容性。该奖项得到了跨部门预测机构的支持流行病预防情报第一阶段（PIPP）项目由生物科学理事会（BIO）、计算机信息科学与工程理事会（CISE）、工程学理事会（ENG）以及社会、行为和经济科学理事会（SBE）联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Intra-host mutation rate of acute SARS-CoV-2 infection during the initial pandemic wave

最初大流行浪潮期间急性 SARS-CoV-2 感染的宿主内突变率

DOI：
10.1007/s11262-023-02011-0
发表时间：
2023-06
期刊：
Virus Genes
影响因子：
1.6
作者：
El;Adhikari, Thamali M.;Tu, Zheng Jin;Cheng, Yu;Leng, Xiaoyi;Zhang, Xiangyi;Rhoads, Daniel;Ko, Jennifer S.;Worley, Sarah;Li, Jing;et al
通讯作者：
et al

Interoperability in a Post- Roe Era: Sustaining Progress While Protecting Reproductive Health Information

后罗伊时代的互操作性：在保护生殖健康信息的同时保持进步