dc.contributor.author |
Fernández-Fontelo, Amanda |
dc.contributor.author |
Moriña, David |
dc.contributor.author |
Cabaña Nigro, Alejandra |
dc.contributor.author |
Arratia, Argimiro |
dc.contributor.author |
Puig, Pedro |
dc.date |
2020 |
dc.date.accessioned |
2022-10-31T02:53:19Z |
dc.date.available |
2022-10-31T02:53:19Z |
dc.date.issued |
2022-10-31 |
dc.identifier |
https://ddd.uab.cat/record/253157 |
dc.identifier |
urn:10.1371/journal.pone.0242956 |
dc.identifier |
urn:oai:ddd.uab.cat:253157 |
dc.identifier |
urn:pmcid:PMC7714127 |
dc.identifier |
urn:pmc-uid:7714127 |
dc.identifier |
urn:pmid:33270713 |
dc.identifier |
urn:oai:pubmedcentral.nih.gov:7714127 |
dc.identifier |
urn:oai:egreta.uab.cat:publications/e5c8f4d4-65d5-4129-9a6a-66281c8a5bb5 |
dc.identifier |
urn:scopus_id:85097122585 |
dc.identifier |
urn:articleid:19326203v15e0242956 |
dc.identifier.uri |
http://hdl.handle.net/2072/525531 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
|
dc.relation |
Instituto de Salud Carlos III COV20/00115 |
dc.relation |
Agencia Estatal de Investigación RTI2018-096072-B-I00 |
dc.relation |
Ministerio de Economía y Competitividad MDM-2014-0445 |
dc.relation |
Agencia Estatal de Investigación TIN2017-89244-R |
dc.relation |
Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-856 |
dc.relation |
PloS one ; Vol. 15 (December 2020), art. e0242956 |
dc.rights |
open access |
dc.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. |
dc.rights |
https://creativecommons.org/licenses/by/4.0/ |
dc.title |
Estimating the real burden of disease under a pandemic situation : The SARS-CoV2 case |
dc.type |
Article |
dc.description.abstract |
The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model. |