Bayesian Monitoring of COVID-19 in Sweden

TL;DR

This paper presents a Bayesian, data-driven compartmental model for monitoring COVID-19 in Sweden, providing real-time estimates of key epidemiological parameters to support public health decisions.

Contribution

It introduces a novel Bayesian filtering approach with a posterior marginal estimator for improved temporal resolution and robustness in disease monitoring.

Findings

Accurate estimates of the effective reproduction number.

Insights into regional infection fatality rates.

Validation against extensive screening data.

Abstract

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.