RAPID: development of a local epidemiological population balance model informed by UAV and WVD data

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Decision making and policy setting by universities and surrounding localities requires knowledge of how people move and interact in the environment. This RAPID project adapts new scientific approaches in population balance modeling to model human movement and interaction on a university campus and a surrounding town with the goal of providing new tools to help develop rational strategies for mitigation and eventual elimination of the novel corona virus, as well as future biological threats. Data for the model input will be obtained from high-definition video footage of public, outdoor areas including green spaces/parks, sidewalks/streets, and campus walkways/congregating spaces analyzed by artificial intelligence algorithms. Highly efficient tools that were originally developed to study complex fluids will enable determination key parameters needed for epidemiological models including effective transmission rates. Epidemiological modeling will be translated into a dashboard for use by policy makers as well as for public education about mitigation strategies. This RAPID project will provide a computational tool and example for use more broadly by communities and in additional and future, challenging public health issues.A multivariate population balance model applied to a college and local municipality will generate key parameters for agent-based epidemiological models. Multivariate balance modeling will be challenged with new data sets of local population density and motion for model parameter estimation using parallel tempering developed under prior and current NSF support. In addition to the usual distinctions of immune, susceptible, exposed, infected, and recovered classes, additional variables to consider include: age, especially relevant for University students, face-covering, inside and outside, and spatial-temporal population distributions afforded by real time updates of aerial (unmanned aerial vehicle) and ground (stationary camera augmented by wearable video devices) surveillance data. While it is common to include coarse-grained information afforded by transportation networks in large-scale epidemiology models, this project will explore opportunities afforded by social force models combined with epidemic population balance modeling. Advanced parallel tempering algorithms will be run on a GPU cluster to challenge the model with daily data streams to update parameters for epidemiological models and scenario projections. A project dashboard will be made available for policy decision making and public education. Broader impacts include computational tools that can be applied to a broad range of public health issues.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.