2021/04/15 by David Moriña, Amanda Fernández‐Fontelo, Moriña, David +7
Computer Science · Mathematics · Medicine · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2104.07575
openalex publication_date 2021/04/15 · openalex created_date 2022/07/22 · openalex updated_date 2026/07/28
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.