We have developed an agent-based model to help you understand how the virus is spread and the effects of various personal and public policies.
NSF RAPID Grant
Development of a local epidemiological population balance model informed by UAV and WVD data (NSF CBET 2040503)
Abstract: Decision making and policy setting by universities and localities requires knowledge of how people move and interact in the environment. This program adapts new scientific approaches in population balance modeling developed under the auspices of the National Science Foundation to model human movement and interaction in our college and town with the goal of providing new tools to help in developing 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, again developed in prior research funded by the National Science Foundation, will enable determining 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 various 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.
ACKNOWLEDGEMENTS: This project is funded by the National Science Foundation CBET #2040503. We gratefully acknowledge the College of Engineering IT system support team as well as the UD Central IT Research Computing group and Prof Michael Mackay for technical support and resources in support of this project, as well as the numerous UD staff, faculty and students who have contributed to and supported this effort.