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：为给公共卫生部门提供区域级决策支持，我们针对瑞典COVID-19疫情构建了数据驱动的房室模型。基于国家医院统计数据，我们推导出参数先验分布，并开发了线性滤波技术，利用每日医疗需求数据驱动仿真模拟。进一步提出后验边际估计方法，该方法既能提升基本再生数估计的时间分辨率，又能通过参数自助法进行稳健性检验。通过计算框架，我们获得了具有预测价值的贝叶斯模型，该模型可提供疾病进程的关键洞见，包括有效再生数、感染致死率及区域免疫水平的估计值。我们利用包括大规模筛查项目结果在内的多源数据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信息，该方法在公共卫生监测与决策支持领域具有特别的应用前景。