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

PLoS One. 2020 Dec 3;15(12):e0242956. doi: 10.1371/journal.pone.0242956. eCollection 2020.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Basic Reproduction Number
  • COVID-19 / economics
  • COVID-19 / epidemiology*
  • COVID-19 / transmission
  • Cost of Illness
  • Disease Notification / statistics & numerical data*
  • Humans
  • Likelihood Functions
  • Markov Chains
  • Models, Statistical*
  • Pandemics / statistics & numerical data*

Grants and funding

This work was co-funded by Instituto de Salud Carlos III (COV20/00115), and the Spanish Ministry of Economy and Competitiveness (RTI2018-096072-B-I00). A.Fernández-Fontelo acknowledges financial support from the German Research Foundation (D.F.G.). D. Moriña acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the Mara de Maeztu Programme for Units of Excellence in R&D (MDM-2014-0445) and Fundacion Santander Universidades. A. Arratia acknowledges support by grant TIN2017-89244-R from MINECO (Ministerio de Economa, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). This work was also processed in the frame CY Initiative of Excellence (grant\Investissements d’Avenir" ANR- 16-IDEX-0008), Project \EcoDep" PSI-AAP2020-0000000013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.