During pandemics, it is critical for decision-makers such as health care officials and governors to foresee potential impacts and make timely decisions. PanViz 2.0 is an interactive visual analytics web application that combines epidemic models and AI-driven analytics to infer the best-fit parameters and enable adaptation to ongoing pandemics at multiple spatial aggregations (nation-wide, state level, and county level). This current decision-support framework system was built upon our earlier PanViz work in public health syndromic surveillance, pandemic preparedness, and decision support for other person spread and mosquito-spread conditions.
PanViz was used extensively during the 2008–2012 pandemic preparedness activities in the United States by numerous counties and states. The enhanced PanViz 2.0 system facilitates the end-user to create simulations for comparing the state-of-the-art epidemiology (SEIR) and deep learning (LSTM) models for observing critical parameters like hospitalization and death rates at multiple spatial levels for policy and decision-making. The system allows interactive intervention planning to perform what-if analysis to assess events (mitigation or exacerbation). Further capabilities include investigating the lag between policy release date and in-effect date and interactive simulations of the effects of taking decision measures.
If you have any questions or need further information, contact us at disc@ou.edu and ebert@ou.edu.
