PIPP Phase I: Robust Epidemic Surveillance and Modeling (RESUME)

The COVID-19 pandemic has shown how epidemiologic modeling can inform decision making in times of crisis and uncertainty. It has also highlighted significant gaps that must be addressed to create ongoing, interdisciplinary collaborations that can provide more effective predictive intelligence for pandemic prevention (PIPP). The Robust Epidemic Surveillance and Modeling (RESUME) team has experience supporting public health stakeholders during the COVID-19 pandemic and drawing on that has identified critical gaps in three broad areas: 1) communication and collaboration among researchers and public health stakeholders, 2) integration of diverse data streams including surveillance data, and 3) foundational work to predict future pathogens and their evolution. To address these three gaps, the project brings together an interdisciplinary team with expertise in epidemiologic modeling, public health, policy and risk analysis, social sciences, decision modeling, artificial intelligence (AI), high-performance computing (HPC), molecular engineering, structural biology, and large-scale data management and assimilation. The investigators will engage modelers and public health stakeholders to broaden participation in collaborative modeling, carry out pilot projects, and develop training modules for generalizable approaches to collaborative pandemic intelligence. The project will develop curricula for pandemic prevention education and workforce development. The training activities and new curricula will help build a convergent and inclusive PIPP capacity by bringing a diverse workforce into the research pipeline from high school through graduate school and enhancing the expertise of the public health workforce. The project will convene workshops with stakeholders and researchers and carry out pilot studies to refine the vision for an interdisciplinary PIPP center. Activities will advance the science and practice in the following three foci: 1) Co-design of policy, implementation, and risk analyses: Early and sustained engagement with public health stakeholders for the co-design of pandemic prevention requirements; Development and application of methods for decision making under deep uncertainty, value of information, and adaptive interventions; Development of novel computational approaches exploiting advances in AI, data management, and HPC methods for creating integrated multi-fidelity, multi-method, and multi-spatiotemporal scale modeling analyses. 2) Robust data for modeling: Development of novel real-time sensors and near real-time data streams from sensor-based air, wastewater, and human monitoring (including for novel pathogens); Integration of sensor, public health surveillance and clinical data through model-based, data assimilation approaches for combining data streams and epidemiological model forecasts; Creation of large-scale open-science data storage and indexing capabilities for epidemiologic modelers. 3) Prediction of future pathogens: Fundamental research in experimental and theoretical pathogen structure and evolution; Scenario development for epidemiological and decision support modeling of emerging pathogens. To support these foci, the project will demonstrate a sustainable simulation, data, decision support, and learning collaborative platform, the Open Science Platform for Robust Epidemic analYsis (OSPREY). The OSPREY platform will serve as crucial PIPP cyberinfrastructure built to leverage investments in forthcoming exascale and increasingly ubiquitous HPC and data resources. 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.