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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Moriña, David, Fernández-Fontelo, Amanda, Cabaña, Alejandra, Puig, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640038/
https://www.ncbi.nlm.nih.gov/pubmed/34857815
http://dx.doi.org/10.1038/s41598-021-02620-5
_version_ 1784609251904192512
author Moriña, David
Fernández-Fontelo, Amanda
Cabaña, Alejandra
Puig, Pedro
author_facet Moriña, David
Fernández-Fontelo, Amanda
Cabaña, Alejandra
Puig, Pedro
author_sort Moriña, David
collection PubMed
description 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 Online
Article
Text
id pubmed-8640038
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-86400382021-12-06 New statistical model for misreported data with application to current public health challenges Moriña, David Fernández-Fontelo, Amanda Cabaña, Alejandra Puig, Pedro Sci Rep Article 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. Nature Publishing Group UK 2021-12-02 /pmc/articles/PMC8640038/ /pubmed/34857815 http://dx.doi.org/10.1038/s41598-021-02620-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Moriña, David
Fernández-Fontelo, Amanda
Cabaña, Alejandra
Puig, Pedro
New statistical model for misreported data with application to current public health challenges
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640038/
https://www.ncbi.nlm.nih.gov/pubmed/34857815
http://dx.doi.org/10.1038/s41598-021-02620-5
work_keys_str_mv AT morinadavid newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges
AT fernandezfonteloamanda newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges
AT cabanaalejandra newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges
AT puigpedro newstatisticalmodelformisreporteddatawithapplicationtocurrentpublichealthchallenges

Ejemplares similares