Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case

Amanda Fernández-Fontelo*, David Moriña, Alejandra Cabaña, Argimiro Arratia, Pere Puig

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article numbere0242956
JournalPLoS ONE
Volume15
Issue number12 December
DOIs
Publication statusPublished - Dec 2020

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