Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/175437
Title: Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case
Author: Fernández Fontelo, Amanda
Moriña, David
Cabaña, Alejandra
Arratia, Argimiro
Puig i Casado, Pere
Keywords: SARS-CoV-2
Epidèmies
Processos de Markov
SARS-CoV-2
Epidemics
Markov processes
Issue Date: 3-Dec-2020
Publisher: Public Library of Science (PLoS)
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.
Note: Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0242956
It is part of: PLoS One, 2020, vol. 15, num. 12, p. e0242956
URI: http://hdl.handle.net/2445/175437
Related resource: https://doi.org/10.1371/journal.pone.0242956
ISSN: 1932-6203
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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