TY - JOUR
T1 - Estimating the real burden of disease under a pandemic situation
T2 - The SARS-CoV2 case
AU - Fernández-Fontelo, Amanda
AU - Moriña, David
AU - Cabaña, Alejandra
AU - Arratia, Argimiro
AU - Puig, Pere
N1 - Publisher Copyright:
© 2020 Fernández-Fontelo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097122585&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0242956
DO - 10.1371/journal.pone.0242956
M3 - Article
C2 - 33270713
AN - SCOPUS:85097122585
SN - 1932-6203
VL - 15
JO - PloS one
JF - PloS one
IS - 12 December
M1 - e0242956
ER -
