低报非统计性时间序列:西班牙的Covid-19发生率 (Bayesian Synthetic Likelihood Estimation for Underreported Non-Stationary Time Series: Covid-19 Incidence in Spain)

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, we explore the performance of Bayesian Synthetic Likelihood to estimate the parameters of a model capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon. The performance of the proposed methodology is evaluated through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community in 2020.

翻译：由于各种原因,处理误报数据的问题在各种情况中非常普遍,Covid-19全球大流行病造成的目前情况是一个明显的例子,由于数据收集问题和无症状病例比例高,官方来源提供的数据并不总是可靠,在这项工作中,我们探讨巴耶斯人合成人相似性如何估计能够处理误报信息的模式的参数,并重建该现象最有可能发生的演变,通过全面模拟研究对拟议方法的绩效进行评估,并通过在2020年重建每个西班牙自治区每周的Covid-19发生率来说明。