按年龄和地区对英格兰的新冠肺炎死亡病例进行即时预测。

Seaman Shaun R, Samartsidis Pantelis, Kall Meaghan, De Angelis Daniela

MRC Biostatistics Unit University of Cambridge Cambridge Cambridgeshire UK.

COVID-19 National Epidemiology Cell UK Health Security Agency London UK.

J R Stat Soc Ser C Appl Stat. 2022 Jun 15. doi: 10.1111/rssc.12576.

Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

了解新冠病毒死亡人数的每日变化轨迹对于如何应对疫情的决策至关重要，但由于死亡发生与报告之间存在延迟，估算这一轨迹变得复杂。在英格兰，延迟通常为几天，但也可能长达数周。这使得近期死亡人数存在相当大的不确定性。在此，我们使用一个考虑报告日效应和延迟分布长期变化的贝叶斯模型，估算了英格兰七个地区五个年龄层的每日死亡人数。我们展示了在多个年龄层（例如在广泛的年龄范围内）延迟分布相同时，该模型如何能够高效地进行计算拟合。