Jersakova Radka, Lomax James, Hetherington James, Lehmann Brieuc, Nicholson George, Briers Mark, Holmes Chris
The Alan Turing Institute London UK.
University College London London UK.
J R Stat Soc Ser C Appl Stat. 2022 Apr 23. doi: 10.1111/rssc.12557.
Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for 'Pillar 2' swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo.
由于有新冠病毒症状者的阳性检测结果报告存在延迟，获取英国新冠病毒区域感染数量的最新信息受到了阻碍。在英国，对于有症状者的“第二支柱”拭子检测，结果整理可能需要长达五天的时间。我们利用漏报过程随时间的稳定性，构建了一个统计时间模型，该模型根据陆续得到的部分计数信息推断最终总数。我们采用贝叶斯方法，为参数提供主观先验，并为感染计数的潜在强度过程构建层次结构。这产生了一个平滑的时间序列表示，即现在预测每日阳性检测数的预期数量，并带有可用于辅助决策的不确定性区间。推理是使用序贯蒙特卡罗方法进行的。
