Department of Econometrics, Statistics and Applied Economics, Riskcenter-IREA, Universitat de Barcelona (UB), Barcelona, Spain.
Departament de Matemàtiques, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain.
BMC Med Res Methodol. 2023 Mar 28;23(1):75. doi: 10.1186/s12874-023-01894-9.
The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process.
The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community.
Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions.
The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.
由于各种原因，在广泛的背景下，处理误报数据的问题非常普遍。当前由全球新冠肺炎大流行引起的情况就是一个明显的例子，由于数据收集问题以及无症状病例比例较高，官方来源提供的数据并不总是可靠的。在这项工作中，提出了一个灵活的框架，目的是量化时间序列中误报的严重程度，并重建过程的最可能演变。
通过全面的模拟研究评估贝叶斯综合似然法在估计基于自回归条件异方差时间序列的模型参数方面的性能，该方法能够处理误报信息，并通过重建西班牙各自治区每周的新冠肺炎发病率来说明最可能的现象演变。
西班牙在 2020 年 2 月 23 日至 2022 年 2 月 27 日期间报告的新冠肺炎病例中，只有约 51%是真实的，显示出各地区误报严重程度存在显著差异。
所提出的方法为公共卫生决策者提供了一个有价值的工具，以便在不同情况下改进疾病演变的评估。
