标题： 瑞典的COVID-19贝叶斯监测 显示英文标题

标题： Bayesian Monitoring of COVID-19 in Sweden

摘要： 为了向公共医疗提供区域决策支持，我们设计了一个以数据驱动的基于仓室模型的瑞典新冠传播模型。 我们从国家医院统计数据中推导出参数先验，并开发了线性滤波技术，以便根据每日医疗需求的数据来驱动模拟。 此外，我们提出了一种后验边缘估计器，该估计器提高了再生数估计的时间分辨率，并通过参数引导程序支持鲁棒性检查。 通过我们的计算方法，我们获得了一个具有预测价值的贝叶斯模型，为疾病进展提供了重要见解，包括有效再生数、病死率以及地区层面的免疫水平的估算。 我们成功地用多个不同来源验证了后验模型，其中包括大规模筛查项目的输出结果。 由于与所需数据相比易于收集且不敏感，我们认为这种方法特别有前景，可以作为支持公共卫生监测和决策的工具。 显示英文摘要

摘要： 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.