New statistical model for misreported data with application to current public health challenges

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 pe...

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Autores principales:	David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c4cf1cc2b6c6404d96e8b7425cc30bb9
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spelling	oai:doaj.org-article:c4cf1cc2b6c6404d96e8b7425cc30bb92021-12-05T12:11:39ZNew statistical model for misreported data with application to current public health challenges10.1038/s41598-021-02620-52045-2322https://doaj.org/article/c4cf1cc2b6c6404d96e8b7425cc30bb92021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02620-5https://doaj.org/toc/2045-2322Abstract 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.David MoriñaAmanda Fernández-FonteloAlejandra CabañaPedro PuigNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Moriña Amanda Fernández-Fontelo Alejandra Cabaña Pedro Puig New statistical model for misreported data with application to current public health challenges
description	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.
format	article
author	David Moriña Amanda Fernández-Fontelo Alejandra Cabaña Pedro Puig
author_facet	David Moriña Amanda Fernández-Fontelo Alejandra Cabaña Pedro Puig
author_sort	David Moriña
title	New statistical model for misreported data with application to current public health challenges
title_short	New statistical model for misreported data with application to current public health challenges
title_full	New statistical model for misreported data with application to current public health challenges
title_fullStr	New statistical model for misreported data with application to current public health challenges
title_full_unstemmed	New statistical model for misreported data with application to current public health challenges
title_sort	new statistical model for misreported data with application to current public health challenges
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c4cf1cc2b6c6404d96e8b7425cc30bb9
work_keys_str_mv	AT davidmorina newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges AT amandafernandezfontelo newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges AT alejandracabana newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges AT pedropuig newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges
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New statistical model for misreported data with application to current public health challenges

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