PIPP Phase I: Collective Intelligence for Pandemic Prediction Prevention and Response

这项流行病预防预测智能（PIPP）第一阶段：开发赠款旨在测试一个新平台的可行性和性能，该平台可以合并目前孤立的工具，以改变传染病监测和公共卫生干预。数字平台将成为一种不断发展的资源，为四个关键的大流行病预防情报领域提供工具包。第一个领域的目标是监测，例如接触者追踪（人类相互作用）和废水病原体检测。第二个领域是诊断，例如疾病的快速检测。第三个领域是开发分析智能，如知识图谱或预测方法。最后一个领域是行动，例如政策和公共卫生建议。作为该平台的一个关键特征，从研究人员和医生到政治家和普通公众的所有利益相关者都将能够向平台提供数据并从中获取信息。如果成功，这样的系统将提供关键的科学证据，并成为一个试验台，通过未来的流行病预防预测智能中心（PIPP）促进进一步的公共卫生调查，该中心将社会科学与以实验室为导向的自然科学相结合，汇集了跨学科的专业知识。除研究外，培训未来的流行病预防工作人员，开展教育社区并使社区参与流行病预防工作的外联活动，将是预防流行病预防方案中心未来活动的重要组成部分。该项目将通过联合公共卫生执行者、社区和传染病、生物工程、统计建模、公共卫生、计算机科学、以人为本的设计、政策、性别平等等方面的专家，为未来的PIPP中心探索一个运作框架，推进流行病情报的融合科学。项目活动将根据以下目标实施：1)创建集体智能网络基础设施原型，并通过桌面模拟在现实世界中进行测试；2)利用知识翻译和原型-社区互动为未来的PIPP中心生成新的知识、计划和基础设施；3)为中心活动和政策制定团队科学方法和道德框架，并建立社区和利益相关者参与指导中心发展。作为发展活动的一部分进行的协作性跨学科讨论将导致确定关于大流行病预防和应对的知识差距、研究挑战以及教育和公众参与的新机会。开发活动将产生一个集体智慧平台原型，为有效的人-技术整合和流行病预测和应对的闭环方法提供重要见解。最后，将产生一个充满道德和以人为本的愿景的关键路线图，并将其转化为实践，作为将参与式设计、团队科学和网络物理系统开发结合在一起的框架。通过这种从实践到学习的方法，该项目将推进大流行病情报科学，并支持教育和培训应对大流行病的工作人员。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。

This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant aims to test the feasibility and performance of a new platform that can merge currently siloed tools, in order to transform infectious-disease surveillance and public-health intervention. The digital platform will be an evolving resource that provides the toolkit for four key pandemic-prevention intelligence areas. The first area targets surveillance, such as contact tracing (human interaction) and wastewater detection of pathogens. The second area is diagnosis, such as rapid testing for disease. The third area is developing analytic intelligence, such as knowledge graphs or prediction methods. The final area is action, such as policy and public health recommendations. As a key feature of the platform, all stakeholders from researchers and medical doctors to politicians and the general public will be able to both contribute data to the platform and draw information from it. If successful, such a system would provide critical scientific evidence and become a testbed to foster further public-health investigations via a future Predictive Intelligence for Pandemic Prevention (PIPP) Center that combines social sciences with lab-oriented natural science and brings together expertise from across disciplines. In addition to research, training to prepare a future pandemic-ready workforce and outreach activities that educate and integrate the community into pandemic prevention efforts will be important parts of future PIPP Center activities.This project will advance convergence science for pandemic intelligence by uniting public-health implementers, the community, and experts in infectious disease, bioengineering, statistical modeling, public health, computer science, human-centered design, policy, and gender, equity to explore an operational framework for a future PIPP Center. Project activities will be implemented according to the following objectives: 1) Create a collective intelligence cyberinfrastructure prototype and test it through a tabletop simulation in a real-world setting; 2) Use knowledge translation and prototype-community interaction to generate new knowledge, plans, and infrastructure for a future PIPP center; 3) Develop a team-science approach and ethics-focused framework for Center activities and policies, and build community and stakeholder engagement to guide Center development. The collaborative, interdisciplinary discussions held as part of the development activities will result in the identification of knowledge gaps, research challenges, and new opportunities for education and public engagement regarding pandemic prevention and response. Development activities will result in a prototype collective intelligence platform that will provide important insights into effective human-technology integration and closed-loop methods of pandemic prediction and response. Finally, a critical roadmap imbued with ethics and people-centered vision will be produced and converted to practice as a framework for stitching together participatory design, team science, and cyber-physical system development. Through this practice-to-learn approach, this project will advance the science of pandemic intelligence and support the education and training of a pandemic-ready workforce.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.