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, Pedro (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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