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Most COVID-19 predictive modeling efforts use statistical or mathematicalmodels to predict national- and state-level COVID-19 cases or deaths in thefuture. These approaches assume parameters such as reproduction time, testpositivity rate, hospitalization rate, and social intervention effectiveness(masking, distancing, and mobility) are constant. However, the one certaintywith the COVID-19 pandemic is that these parameters change over time, as wellas vary across counties and states. In fact, the rate of spread over region,hospitalization rate, hospital length of stay and mortality rate, theproportion of the population that is susceptible, test positivity rate, andsocial behaviors can all change significantly over time. Thus, thequantification of uncertainty becomes critical in making meaningful andaccurate forecasts of the future. Bayesian approaches are a natural way tofully represent this uncertainty in mathematical models and have becomeparticularly popular in physics and engineering models. The explicitintegration time varying parameters and uncertainty quantification into ahierarchical Bayesian forecast model differentiates the Mayo COVID-19 modelfrom other forecasting models. By accounting for all sources of uncertainty inboth parameter estimation as well as future trends with a Bayesian approach,the Mayo COVID-19 model accurately forecasts future cases and hospitalizations,as well as the degree of uncertainty. This approach has been remarkablyaccurate and a linchpin in Mayo Clinic's response to managing the COVID-19pandemic. The model accurately predicted timing and extent of the summer andfall surges at Mayo Clinic sites, allowing hospital leadership to manageresources effectively to provide a successful pandemic response. This model hasalso proven to be very useful to the state of Minnesota to help guide difficultpolicy decisions.

A Hierarchical Bayesian Model for Stochastic Spatiotemporal SIR Modeling and Prediction of COVID-19 Cases and Hospitalizations