  In an effort to provide regional decision support for the public healthcare,
we design a data-driven compartment-based model of COVID-19 in Sweden. From
national hospital statistics we derive parameter priors, and we develop linear
filtering techniques to drive the simulations given data in the form of daily
healthcare demands. We additionally propose a posterior marginal estimator
which provides for an improved temporal resolution of the reproduction number
estimate as well as supports robustness checks via a parametric bootstrap
procedure.
  From our computational approach we obtain a Bayesian model of predictive
value which provides important insight into the progression of the disease,
including estimates of the effective reproduction number, the infection
fatality rate, and the regional-level immunity. We successfully validate our
posterior model against several different sources, including outputs from
extensive screening programs. Since our required data in comparison is easy and
non-sensitive to collect, we argue that our approach is particularly promising
as a tool to support monitoring and decisions within public health.
