PIPP Phase I: Computational Theory of the Co-evolution of Pandemics, (Mis)information, and Human Mindsets and Behavior

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Epidemiological models are used to predict the spread of highly contagious and lethal diseases such as COVID-19. Public health officials use such models to inform pandemic response policies and advisories. Yet these models require a rigorous scientific foundation about human psychology to better predict people’s responses to information and policies about pandemics. The recent COVID-19 pandemic illustrates the central role of human decision making and behavior in the spread of such a transmissible disease. People’s decisions regarding social isolation, social distancing, mask wearing, hand washing, and vaccination are correlated with the rate at which the COVID-19 virus spreads or the seriousness of getting infected. People have different individual mindsets, and these can vary across different regions and subgroups, so different groups of people respond differently to messaging and mandates and those responses change over time. There is also an ongoing scientific debate about the degree to which pandemic information or misinformation, or the perceived credibility of information sources, influences the degree to which people change their behavior. To address these scientific needs, this project involves activities to develop a multidisciplinary research core and agenda and to develop a strong plan for a cohesive research center for Predictive Intelligence for Pandemic Prevention. The activities include exploratory research on computational models of human psychology, information flow and influence, and resulting pandemic transmission. The project will also support the training and mentoring of graduate students who represent the next generation of researchers tackling these global challenges.This project uses computational theories and models to examine the fundamental interdependent evolution of infection, behavior, and information at multiple levels and drawing upon multiple disciplines in order to support improved pandemic intelligence, prediction, explanation, and countermeasures. The project is organized into (1) interdisciplinary, strategic research thrusts to Accelerate Convergent Science towards the Grand Challenge, (2) three invitational meetings to draw in diverse researchers to address focal research topics and research questions, to fill in gaps in the Research Challenges, and develop a strong research and education agenda for a cohesive PIPP center, and (3) Pilot Studies to Demonstrate Feasibility of integrated computational models of information, human psychology, and pandemic transmission. For the pilot research, a multidisciplinary team combines empirical assessments with computational cognitive models in an agent-based modeling system. For data the investigators draw on vaccination discussions in mass media, Twitter, geolocated timeseries data on vaccination rates, infection, death and recovery rates, state and national mandates regarding COVID-19 policies about vaccination and mask wearing from February 2020 through December 2021 in the United States. These data will be segmented by state and major cities within those states. 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.