PIPP Phase I: Computational Foundations for Bio-social Modeling of Unseen Pandemics

Arizona State University, Scottsdale AZ

Investigators

Abstract

Pandemics unfold in a social, behavioral, and decision-making context that alters the geospatial patterns of spread, depending on an existing underlying landscape of risk and adaptive behavior. Layering socioeconomic factors into traditional predictive modeling frameworks is not sufficient to understand this complexity, nor does it account for the dynamics and vicissitudes of human behavior and free-will. Unexpected human behaviors play a major role, as well as broader factors such as vaccine availability, seasonal effects from human contact patterns, viral environmental persistence, and federal and state-level policy changes around masking and business closures. Enumerating a finite list of factors that should form the basis of a predictive model itself seems like a grand challenge. This project will advance modeling as a continuous, iterative, and dynamic component of pandemic response, where incremental predictions are far more robust, and approaches that innately allow for complexity, adaptation, and surprise can be expected to be operationally useful. Pandemic prevention for unseen pandemics requires several interconnected efforts across immunology, mechanistic modeling, data-driven modeling, and understanding sociopolitical contexts of decision making. Technical aspects of the project include machine learning based tools for predicting immune response from pathogen mutations, switching dynamical systems based models of time-series for fast adaptation, adaptive population sampling techniques, and model predictive control methods for designing behavioral interventions. The project will develop integrative protocols and frameworks that a) leverage techniques for using binding patterns of pathogens for never-before-seen viruses and advances in wastewater-based epidemiology, b) understand the variation in performance of predictive models over geospatial scales using regularizing models, c) design effective interventions under resource constraints, and d) understand their impact on policy making. 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), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG). 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.