Bayesian Monitoring of COVID-19 in Sweden

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 deduce parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally put forward an optimization scheme 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. Taken together we obtain a computationally efficient Bayesian approach 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.