论文标题
使用用于瑞典的共同19岁死亡的领先指标的幕后指标
Nowcasting with leading indicators applied to COVID-19 fatalities in Sweden
论文作者
论文摘要
传染病监测数据的实时分析，例如，以报告的病例或死亡的时间序列的形式，对于获得对当前不良健康事件（例如COVID-19大流行）当前动态的情境意识至关重要。通过报告延迟导致最新时间点（例如，几天或几周）报告事件数量的延迟，这种实时分析变得复杂。这可能会导致口译员，例如媒体或公众的误解，就像瑞典共同大流行期间报道的死亡的时间序列一样。通过使用有关过去的报告延迟的信息，使用有关当前报告事件的不完整时间序列的Nowcasting方法提供了完整的事件数量的实时估算。在这里，我们考虑将瑞典与共同19岁的死亡人数相关的人数。我们提出了一种灵活的贝叶斯方法，通过合并回归组件来扩展现有的现有方法，以适应由报告案例和ICU入学量的时间序列等领先指标提供的其他信息。通过回顾性评估，我们表明，与现有方法相比，瑞典Covid-19的病例死亡人士的病例死亡人数的象征性绩效提高了ICU入院率。
The real-time analysis of infectious disease surveillance data, e.g., in the form of a time-series of reported cases or fatalities, is essential in obtaining situational awareness about the current dynamics of an adverse health event such as the COVID-19 pandemic. This real-time analysis is complicated by reporting delays that lead to underreporting of the number of events for the most recent time points (e.g., days or weeks). This can lead to misconceptions by the interpreter, e.g., the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events by using information about the reporting delays from the past. Here, we consider nowcasting the number of COVID-19-related fatalities in Sweden. We propose a flexible Bayesian approach, extending existing nowcasting methods by incorporating regression components to accommodate additional information provided by leading indicators such as time-series of the number of reported cases and ICU admissions. By a retrospective evaluation, we show that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.
