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

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.