In an effort to provide regional decision support for the public healthcare, we design a data-driven compartmentbased 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 enables a refined resolution of the reproduction number estimate, and which also improves substantially on our confidence in the overall results thanks to 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.