School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.
Climate and Atmosphere Research Centre, The Cyprus Institute, Aglantzia, Cyprus.
Biometrics. 2023 Sep;79(3):2537-2550. doi: 10.1111/biom.13810. Epub 2022 Dec 27.
The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID-19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision-making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15-month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID-19 and future epidemics.
COVID-19 大流行凸显了延迟报告是有效疾病监测和决策的重大障碍。在缺乏及时数据的情况下，可以采用考虑到延迟的统计模型来对病例或死亡进行实时预测和预测。 我们讨论了 COVID-19 和其他疾病可用数据中系统和随机变异性的四个关键来源，并批判性地评估了当前最先进的方法在适当分离和捕获这种变异性方面的情况。我们提出了一种通用的分层方法来纠正 COVID-19 的延迟报告，并将其应用于每日英国医院死亡人数，从而得出一个灵活的预测工具，可用于更好地为大流行决策提供信息。我们根据理论灵活性和从模拟现实操作场景的 15 个月滚动预测实验中得出的定量指标，将这种方法与竞争模型进行了比较。 基于预测准确性、偏差和精度方面的一致领先，我们认为这种方法是纠正 COVID-19 和未来疫情延迟报告的一个有吸引力的选择。
