Department of Mathematics, Stockholm University, Stockholm, Sweden.
PLoS Comput Biol. 2022 Dec 7;18(12):e1010767. doi: 10.1371/journal.pcbi.1010767. eCollection 2022 Dec.
The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance 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 and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation 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.
实时分析传染病监测数据对于了解当前重大公共卫生事件（如 COVID-19 大流行）的动态至关重要。例如，对报告病例或死亡人数的时间序列进行分析时，由于报告延迟，导致最近时间点的事件总数报告不足。这可能会导致误解，例如媒体或公众，就像 COVID-19 大流行期间瑞典报告的死亡人数时间序列那样。即时预测方法使用当前报告事件的不完整时间序列和过去报告延迟的信息，提供完整事件数量的实时估计。在本文中，我们提出了一种新的贝叶斯即时预测方法，应用于瑞典与 COVID-19 相关的死亡人数。我们以报告病例和 ICU 入院人数的时间序列的形式纳入了额外的信息作为领先信号。我们通过回顾性评估表明，与现有方法相比，将 ICU 入院作为领先信号纳入 COVID-19 病例死亡率的即时预测可以提高其性能。
