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

Author:

Fernández-Fontelo AmandaORCID,Moriña David,Cabaña AlejandraORCID,Arratia ArgimiroORCID,Puig Pere

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.

Funder

Instituto de Salud Carlos III

Ministerio de Economía, Industria y Competitividad, Gobierno de España

Fundación Banco Santander

Agència de Gestió d’Ajuts Universitaris i de Recerca

Deutsche Forschungsgemeinschaft

CY Initiative of Excellence

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

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1. Modelización estadística para la estimación y predicción de la incidencia de Covid-19 en España;REVISTA ESPAÑOLA DE COMUNICACIÓN EN SALUD;2024-01-06

2. El papel de los modelos matemáticos en la Estrategia de Vacunación frente a COVID-19 en España;REVISTA ESPAÑOLA DE COMUNICACIÓN EN SALUD;2024-01-06

3. COVID-19 incidence estimates and forecast by metaprediction for the Comunidad de Madrid *;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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