New statistical model for misreported data with application to current public health challenges
Moriña, David (Centre de Recerca Matemàtica)
Fernández-Fontelo, Amanda (Humboldt-Universität zu Berlin)
Cabaña Nigro, Alejandra (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Puig, Pedro (Universitat Autònoma de Barcelona. Departament de Matemàtiques)

Date:	2021
Abstract:	The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.
Grants:	Instituto de Salud Carlos III COV20/00115 Agencia Estatal de Investigación RTI2018-096072-B-I00 Agencia Estatal de Investigación CEX2020-001084-M
Rights:	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.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	China ; Computer Simulation ; COVID-19 ; Humans ; Models, Statistical ; Papillomavirus Infections ; Public Health ; Spain ; Time Factors
Published in:	Scientific reports, Vol. 11 (December 2021) , art. 23321, ISSN 2045-2322

DOI: 10.1038/s41598-021-02620-5
PMID: 34857815

10 p, 1.1 MB

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