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.

本研究提出一种基于经检测呈阳性的感染者监测数据与住院收治数据的近乎实时时变确认率（time-varying ascertainment rate）估算新方法，此外还探究了该估算结果的年龄依赖性。该确认率估算基于贝叶斯定理（Bayes theorem），易于计算并可应用于两类场景：(i) 作为疾病传播机理模型的组成部分；(ii) 近乎实时地估算未报告感染病例数及其占比变化，可作为未被察觉的疫情暴发出现时的早期预警信号之一。本研究还包含一项针对捷克共和国COVID-19疫情的案例分析，该案例展示了确认率估算值在回顾性分析、疫情监测、不同疫情波次间差异阐释、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有效性的具体实践。捷克监测数据显示，在2020年3月初至2021年5月末的监测周期内，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COVID-19疫情传播的机理模型中，确认率可帮助我们阐释包括新增病例、住院病例与死亡病例在内的所有基本仓室之间的关联。