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.

翻译：为了为公共医疗系统提供区域决策支持，我们设计了基于数据驱动的瑞典COVID-19传播仓室模型。基于国家医院统计数据，我们推导了参数先验分布，并开发了线性滤波技术，利用每日医疗需求数据驱动模拟。此外，我们提出了一种后验边际估计方法，该方法可提高再生数估计的时间分辨率，并通过参数自举程序支持稳健性检验。通过计算方法，我们获得了具有预测价值的贝叶斯模型，该模型为疾病进展提供了重要见解，包括有效再生数、感染致死率以及区域免疫水平的估计。我们利用多个不同来源（包括大规模筛查计划的输出结果）成功验证了后验模型。由于所需数据易于获取且不涉及敏感性，我们认为该方法作为支持公共卫生监测与决策的工具尤其具有前景。