Nowcasting with leading indicators applied to COVID-19 fatalities in Sweden
利用领先指标即时预测瑞典新冠死亡人数AI 解读 ↓
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利用领先指标即时预测瑞典新冠死亡人数
- 研究问题
- 死亡报告延迟使近期记录不完整，如何利用历史延迟信息及领先指标，即时估计瑞典当前完整的新冠死亡人数？
- 方法与思路
- 作者扩展贝叶斯即时预测方法，在报告延迟模型中加入回归成分，利用报告病例数和重症监护入院数等领先时间序列。
- 材料陈述的贡献
- 摘要称回溯评估显示，加入重症监护入院信号后，瑞典新冠死亡人数的即时预测表现优于所比较的既有方法。
- 阅读关注点
- 应核对报告延迟机制、领先信号的实时可用性、回溯评估是否遵守信息时点，以及预测误差与不确定性的评价方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未报告改善数值，也未说明该结果能否推广到其他地区或疫情阶段。
原文摘要
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.