Título:
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Estimating the real burden of disease under a pandemic situation : The SARS-CoV2 case
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Autor/a:
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Fernández-Fontelo, Amanda; Moriña, David; Cabaña Nigro, Alejandra; Arratia, Argimiro; Puig, Pedro
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Abstract:
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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. |
Fecha de creación:
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31-10-2022 |
Derechos:
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open access
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https://creativecommons.org/licenses/by/4.0/ |
Tipo de documento:
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Article |
Editor:
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Uri:
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https://ddd.uab.cat/record/253157
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