PIPP Phase I: Coupling Predictive Intelligence with Adaptive Response to Create Pandemic-Resilient Cities

当第一批新冠肺炎病例出现在美国各地的城市时，它引发了一个指数级传播过程，使用现有技术、干预措施和解决方案无法逆转这一过程。随着气候变化加速出现具有大流行潜力的病原体，城市和城市地区将提供收集和传播新病原体的媒体和渠道。病原体感知/监测和流行病学预测方面的最新进展，以及在使用非药物干预措施减少新冠肺炎传播方面的早期成功表明，如果反馈足够本地化和快速，预测智能和适应性反应之间的反馈循环可以使城市能够有效地减弱大流行的威胁。该项目的总体目标是为建立一个中心奠定基础，该中心将数学/计算建模与工程、公共卫生和公众参与结合起来，探索城市规模反馈回路的设计和原型，以主动降低具有大流行潜力的病原体的传播率。拟议的PIPP第一阶段中心发展活动将包括有针对性的研究项目、研讨会和劳动力发展，包括指导四名研究生和在俄勒冈州立大学建立研究生轮换计划，该计划将提供跨学科的大流行科学培训，并使科学家、工程师、公共卫生专业人员和利益相关者之间能够开展和促进关于大流行动力学的双向培训和交流。该PIPP第一阶段项目将为一个中心奠定基础，该中心将应对将城市从大流行扩大者转变为衰减者的“巨大挑战”。为了推进这一目标，项目组建议设计、构建和评估预测智能和自适应响应之间的反馈回路，通过利用城市系统中相互作用的组件网络来降低城市和城市地区的大流行威胁。这项研究的具体目标是：1)与公共卫生专业人员和社区领导人建立和扩大社区-学术伙伴关系，以促进城市和城市地区的大流行预测智能和适应性反应；2)开发可以模拟城市系统中跨越流行病临界点的过程的数学和计算模型；以及3)设计和制作反馈环路的原型，可以预测和减少传染病在城市和城市地区的传播。拟议研究的成功完成有可能通过建立社区-学术伙伴关系产生变革性影响，以开发和验证疾病传染预测-反应系统，并评估其有效性和可采纳性。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价值和更广泛的影响审查标准进行评估，被认为值得支持。

When the first cases of COVID-19 appeared in cities across the United States, it triggered a process of exponential spread that could not be reversed using available technologies, interventions, and solutions. As climate change accelerates the emergence of pathogens with pandemic potential, cities and urban areas will provide the media and conduits that collect and transmit novel pathogens. Recent advances in pathogen sensing/surveillance and epidemiological forecasting along with the early success in the use of non-pharmaceutical interventions to reduce the spread of COVID-19 suggest that feedback loops between predictive intelligence and adaptive response could enable cities to efficiently attenuate pandemic threats if the feedback is sufficiently localized and rapid. The overarching goal of this project is to lay the foundation for the establishment of a Center that will combine and integrate mathematical/computational modelling with engineering, public health, and public engagement to explore the design and prototyping of city-scale feedback loops that could proactively attenuate the rates of transmission of pathogens with pandemic potential. The proposed PIPP Phase I Center development activities will include targeted research projects, workshops, and workforce development including the mentoring of four graduate students and the establishment of a graduate student rotation program at Oregon State University that will provide cross-training in transdisciplinary pandemic science and enable the development and facilitation of bi-directional trainings and exchanges on pandemic dynamics between scientists, engineers, and public health professionals and stakeholders.This PIPP Phase I project will lay the foundation for a Center that addresses the “Grand Challenge” of transforming cities from pandemic amplifiers to attenuators. To advance this goal, the project team proposes to design, build, and evaluate feedback loops between predictive intelligence and adaptive response that could attenuate pandemic threats in cities and urban areas by leveraging the networks of interacting components in urban systems. The specific objectives of the research are to: 1) Build and scale up community-academic partnerships with public health professionals and community leaders to advance pandemic predictive intelligence and adaptive response in cities and urban areas; 2) Develop mathematical and computational models that could simulate the process of stepping back across epidemic tipping points in urban systems; and 3) Design and prototype feedback loops that could predict and attenuate the transmission of infectious diseases in cities and urban areas. The successful completion of the proposed research has the potential for transformative impact through the establishment of community-academic partnerships to develop and validate disease contagion prediction-response systems and evaluate their effectiveness and adoptability. 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.