Swedish Institute for Social Research, Stockholm University, 106 91 Stockholm, Sweden.
Department of Finance, Stockholm School of Economics, 113 83 Stockholm, Sweden.
Int J Environ Res Public Health. 2023 Feb 9;20(4):3040. doi: 10.3390/ijerph20043040.
The COVID-19 pandemic has demonstrated the importance of unbiased, real-time statistics of trends in disease events in order to achieve an effective response. Because of reporting delays, real-time statistics frequently underestimate the total number of infections, hospitalizations and deaths. When studied by event date, such delays also risk creating an illusion of a downward trend. Here, we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the "removal method"-a well-established estimation framework in the field of ecology.
新冠疫情大流行表明，为了做出有效应对，及时掌握疾病事件趋势的无偏真实统计数据非常重要。由于报告存在延迟，实时统计数据往往会低估感染、住院和死亡的总人数。如果按事件发生日期进行研究，这种延迟还可能造成趋势下降的假象。在这里，我们描述了一种统计方法，用于根据历史报告延迟来预测真实的日数量及其不确定性。该方法考虑了滞后观察到的分布模式。它源自“去除法”——生态学领域中一个成熟的估计框架。
