NSF Award
2436120
Epidemics arise from interactions between pathogens and human hosts, where the pathogen influences human behavior and human behavior influences the spread of the pathogen. The models used to predict pathogen spread do not include the complexity of interactions between disease and human behavior but instead focus on biological processes and policy interventions. However, disease transmission depends on people’s behaviors, which are shaped by their perceptions of risk from the disease and from health interventions, as well as by the opinions and behaviors of the other people around them. This project will contribute to the development of mathematical epidemiological models that better represent the complexities of the human response to disease and that can be used to evaluate the relative impacts of public health policies on disease dynamics. The project will be focused on understanding respiratory diseases such as COVID-19, seasonal flu, and bird flu, but can be readily modified to be broadly applicable to other infectious diseases such as HIV or Ebola. The project will contribute to existing national COVID-19 and Flu Scenario Modeling Hubs that are working to better predict and understand the dynamics of infectious disease and to contribute to policy interventions. The Investigators will disseminate the results and foster connections with the disease modeling community through a workshop for public health professionals and will engage the public through production of educational music videos targeted at the broader community<br/><br/>The complexity of human behavior is not well represented in epidemiological models, contributing to reduced skill and utility of model forecasts. While some epidemiological models represent human behavioral responses using a few static parameters, the Investigators will construct models of human behavior and policy processes that update dynamically to represent the dependence of human responses to the evolving state of the epidemic. Human cognition, social and policy responses will be represented using a system of differential equations linked with a traditional Susceptible-Exposed-Infected-Recovered epidemiological model using infectious respiratory diseases such as SARS-CoV-2 and H5N1 as model systems. Adoption of protective behaviors (vaccination, physical distancing) will be a function of risk perceptions (from disease and health interventions), health policies (lockdowns, vaccine mandates), and the behavior of other people (social norms). Policy interventions and adoption of protective behaviors mediate disease spread and impacts (infections and deaths) that influence human behavioral and policy responses. Mathematical novelty arises because cognition depends upon the history of infection, so the differential equations have past-dependence, generating differential integral equations. Model outputs will be used to analyze the sensitivity of and uncertainty in epidemic forecasts that arise from human risk perceptions, social influence, protective behaviors, and policy interventions. This project will advance the disease modeling community’s capability to analyze the interlinked dynamics of human social systems and infectious disease, increase the impact of social science on the disease modeling community, and will develop analysis methods for the complex and time-dependent interactions that arise from linkages of disease dynamics with social systems. <br/><br/>This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD).<br/><br/>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.
