Estimated Covid-19 burden in Spain : ARCH underreported non-stationary time series
Moriña, David (Universitat de Barcelona)
Fernández-Fontelo, Amanda (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Cabaña Nigro, Alejandra (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Arratia, Argimiro (Universitat Politècnica de Catalunya)
Puig i Casado, Pere (Centre de Recerca Matemàtica)

Data:	2023
Resum:	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. The online version contains supplementary material available at 10. 1186/s12874-023-01894-9.
Ajuts:	Agencia Estatal de Investigación CEX2020-001084-M Agencia Estatal de Investigación PID2021-123733NB-I00 Agencia Estatal de Investigación RTI2018-096072-B-I00 Agencia Estatal de Investigación IJC2020-045188I
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Continuous time series ; Mixture distributions ; Under-reported data ; ARCH models ; Infectious diseases ; Covid-19 ; Bayesian synthetic likelihood
Publicat a:	BMC Medical Research Methodology, Vol. 23 (March 2023) , art. 75, ISSN 1471-2288

DOI: 10.1186/s12874-023-01894-9
PMID: 36977977

8 p, 1.7 MB

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