  Most COVID-19 predictive modeling efforts use statistical or mathematical
models to predict national- and state-level COVID-19 cases or deaths in the
future. These approaches assume parameters such as reproduction time, test
positivity rate, hospitalization rate, and social intervention effectiveness
(masking, distancing, and mobility) are constant. However, the one certainty
with the COVID-19 pandemic is that these parameters change over time, as well
as vary across counties and states. In fact, the rate of spread over region,
hospitalization rate, hospital length of stay and mortality rate, the
proportion of the population that is susceptible, test positivity rate, and
social behaviors can all change significantly over time. Thus, the
quantification of uncertainty becomes critical in making meaningful and
accurate forecasts of the future. Bayesian approaches are a natural way to
fully represent this uncertainty in mathematical models and have become
particularly popular in physics and engineering models. The explicit
integration time varying parameters and uncertainty quantification into a
hierarchical Bayesian forecast model differentiates the Mayo COVID-19 model
from other forecasting models. By accounting for all sources of uncertainty in
both 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 remarkably
accurate and a linchpin in Mayo Clinic's response to managing the COVID-19
pandemic. The model accurately predicted timing and extent of the summer and
fall surges at Mayo Clinic sites, allowing hospital leadership to manage
resources effectively to provide a successful pandemic response. This model has
also proven to be very useful to the state of Minnesota to help guide difficult
policy decisions.
