RAPID: Modeling the Coupled Social and Epidemiological Networks that Determine the Success of Behavioral Interventions on Limiting Spread of COVID-19

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Abstract:Ideas and viruses can spread in human populations through different forms of interaction. Social distancing is an idea that, when enacted, can lower disease transmission risk and slow the spread of infection in a population. In the case of the current COVID-19 outbreak, in the absence of ready vaccines and medical treatments, social distancing is our best line of defense. The prevalence of social distancing behaviors can depend, however, on the mix of social and geographic communities and their social norms, influencing spread dynamics in schools, social media, work environments, and among friends and family. Members of some social communities (e.g. social media friend groups) may share values and beliefs together without necessarily being in close geographic proximity. Alternatively, people can come into physical proximity—at work, shops, beaches, sporting events— without sharing strongly-held beliefs. Sometimes, even strangers may copy certain visible behaviors, such as wearing protective masks at the grocery store. A pandemic is both a biological and social phenomenon. This work will develop practical tools (models) that predict the interaction between collective behavior and the spatiotemporal dynamics of disease spread. This will enable more accurate predictions of medical resources the population will need over time. Public health measures can target not only individual behavior but also collective behavior, which may require different incentives and nudges, such that public health messaging can be maximally beneficial. Results from the project will also be shared through a public webinar on the role of mathematics in pandemic preparedness.This work to address these gaps will involve two different types of mathematical modeling efforts. The first type will rely on designing a system ordinary differential equations (ODEs) to capture both disease dynamics and social influence. This ODE model will assume that mass action average rates of transition between both disease and belief states are sufficient to gain insight, producing quantitative characterizations to describe how belief dynamics interact with disease prevalence in a community as both progress over time. The second type will rely on designing coupled multi-layer networks in which one layer captures social influence and the other captures physical contact and disease transmission. This model will explore dynamic connections among individuals within each layer, where the strength of contact can shift based on the state and neighbors of the same individual in the other layer. This second model, by focusing on particular network structures will complement the insights about average behaviors gained by the ODE model and provide insight into the different roles individuals may play in shifting community perception and/or spreading infection.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.