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RAISE: IHBEM: Human Behavior Driven Mathematical Modeling and Forecasting of Respiratory Disease Transmission in Urban Settings

Abstract

Human behavior plays a central role in the transmission of respiratory pathogens such as SARS-CoV-2 and influenza; however, realistic representation of many behavioral processes is lacking in existing epidemiological models, which impedes accurate simulation and forecasting of disease spread. This project will use behavior theories and detailed data to develop behavior-driven epidemic models, study the transmission dynamics of COVID-19, and generate improved forecasting systems in urban settings. Studies will focus on New York City (NYC), a densely populated metropolitan area with large socioeconomic disparities that often experiences outbreaks earlier than surrounding regions. The proposed model will incorporate contact patterns indoors, where most transmission occurs, the adoption of protective measures such as mask-wearing, and reactive behavior change in response to infection risk. Research results will deepen understanding of behavior-disease interaction and produce next-generation predictive models for emerging respiratory diseases with validated forecasting accuracy. These efforts will fundamentally improve disease model realism by accurately incorporating behavior into mathematical models and improve the accuracy of respiratory disease forecasts. The developed forecasting systems can be deployed in real time to support epidemic control in the event of public health emergency. Research findings will be disseminated promptly to federal and local public health authorities leveraging ongoing collaborations to translate research into strategies for disease prevention and mitigation. The project will have long-term benefits for capacity building in pandemic preparedness and response. This project will be supported by rich and diverse datasets including neighborhood level COVID-19 data, detailed foot traffic records, mask-wearing survey data, socio-economic indicators, and behavioral characteristics collected from surveys in NYC neighborhoods. Proposed studies are organized around three synergistic research objectives: 1) use of core behavioral science theories – namely temporal discounting, loss aversion, agency, and norms and deviation – to quantify risk-driven behavior change; 2) incorporation of dwell time and crowdedness in different indoor settings and population-level masking into a metapopulation epidemic model at the neighborhood scale; 3) development of a predictive model for COVID-19 with behavior-disease feedbacks and systematic evaluation of its predictive skill through retrospective forecasts. The project will employ a breadth of interdisciplinary skills in mathematical modeling, statistical inference, behavior science, data science, and infectious disease epidemiology. These efforts will produce novel mathematical models incorporating human behaviors that enable improved operational forecasting of respiratory diseases. This project is jointly funded by the Division of Mathematical Science (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE). This project was also co-funded in collaboration with the CDC’s Center for Forecasting and Outbreak Analytics to support research projects to further advance federal infectious disease modeling, prevention and response capabilities. 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.

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Funding Source

Project Period

2022-2026

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