Data and R code.

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.

本研究提出了一种基于阳性感染病例监测及住院收治数据的准实时时变检出率（ascertainment rate）估计新方法，同时探讨了该估计值的年龄依赖性。本检出率估计方法基于贝叶斯定理（Bayes theorem），计算简便，可应用于两大场景：其一，作为疾病传播机理模型的组成部分；其二，在疑似暴发出现时，作为早期预警信号之一，准实时估算未报告感染病例数及其占比的变化情况。本研究还包含捷克共和国2019冠状病毒病（COVID-19）疫情的案例分析，该案例展示了检出率估计值在以下场景中的应用：回顾性分析、疫情监测、不同疫情波次间差异的解释、国家防疫系统中的应用，以及基于捷克全国监测数据集评估非药物干预措施的有效性。捷克监测数据显示，在2020年3月初至2021年5月末的监测期内，老年人群因严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染住院的概率是非老年人群的12倍。在捷克境内COVID-19传播的机理模型中，检出率可用于阐明新增病例、住院病例与死亡病例等所有核心模型隔间间的关联。