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.
翻译:为了努力为公共医疗保健提供区域决策支持,我们设计了瑞典COVID-19的数据驱动区划模型。我们从国家医院统计数据中推导参数前缀,我们开发线性过滤技术,驱动以日常医疗需求形式提供的模拟数据。我们进一步提出了一个优化计划,使复制数字估计得到更好的解析,并大大增强了我们对全结果的信心,这要归功于一个模拟靴子捕捉程序。我们共同获得了一种计算高效的贝叶斯预测值方法,该方法为疾病的发展提供了重要的洞察力,包括有效生殖数、感染死亡率和地区一级豁免的估计。我们成功地根据多种不同来源,包括广泛的筛选方案的产出,验证了我们的远地点模型。由于我们所需要的数据比较容易收集,而且不敏感,我们认为我们的方法特别有希望成为支持公共卫生内部监测和决策的工具。