Predictive Modeling for Campus

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Predictive Modeling of People Movement and Disease Spread on Campus under COVID and Pandemic Situations Case ID: UA21-051 Invention: A lack of information is perhaps the crux of every problem involving public policy. In 2020, COVID-19 emphasizes the lack of data and information with which public figures attempt to make decisions. Ideally, public policy, including policy for large institutions, would be made with a crystal ball and 100% certainty of the effects of the policy. Because this is impossible, simulations try to iterate and propagate key assumptions over time. This invention provides policy makers a novel simulation based on a student-mobility model and a disease propagation in tandem. Student mobility meaning a GIS map overlaid with daily student schedules including classrooms, buildings, dorms, and so on, while the disease propagation model shows student health, shedding rate of the disease, number of infected students in various locations, probabilities of disease transmissions (airborne, droplet, etc.). The invention will allow administrators to get some insight into the impact of policy decisions. Background: Computer models are used in applications like tensile-strength modeling of a steel beam to fluid modeling of biohazardous material in a given scenario. All of these computer models are run for the same reason: it is too expensive to run the experiment physically. This is true for COVID-19 and other diseases; the human cost and organizational cost is too expensive. It is imperative that administrators and policy makers are not ignorant to the effects of their policy decisions, nor should they be paralyzed because the effects of that paralysis may be equally deadly. Accurate models give those policy makers a reasonable glimpse of potential policy outcomes. There is a large need for these models in many areas and, in 2020, this could be the difference between locking down forever and changing input parameters to get people back where they need to be. While there are many models considering crowd control or disease transmission, this model integrates all of the data available to the university to provide a holistic presentation of campus reactions to policy changes.