Department of Computer Science and Engineering, Guangdong Ocean University, Zhanjiang, People's Republic of China.
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
PLoS One. 2024 May 21;19(5):e0303861. doi: 10.1371/journal.pone.0303861. eCollection 2024.
The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages.
To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022.
The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy.
As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.
病死率是指导传染病流行期间公共卫生政策的关键指标。对于 COVID-19,确诊病例的年龄结构随时间变化,这对病死率的实时估计产生了重大影响。即使在不同年龄段的病死率保持不变的情况下,确诊病例向年轻年龄段的转移也可能导致病死率的“虚假下降”。
为了解决这个问题,我们提出了一种标准化的实时病死率估计器。进行了一项模拟研究来评估估计器的性能。该方法应用于 2020 年 3 月至 2022 年 5 月德国 COVID-19 的实时病死率估计。
模拟结果表明,该估计器在所有情况下都能提供疾病病死率的准确趋势,而现有的估计器在时间年龄分布发生变化时可能会传递出对实际情况的误导性信号。对德国数据的应用表明,在实施“与 COVID 共存”策略时,病死率有所增加。
由于许多国家选择与冠状病毒共存,因此频繁检查病死率至关重要。