Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag
针对英国新冠检测结果报告延迟问题，利用报告滞后过程的稳定性，构建贝叶斯统计模型推断最终阳性总数，生成带不确定性的每日即时预测，辅助决策。
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.