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

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

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.

Suggested Citation

  • Amanda Fernández-Fontelo & David Moriña & Alejandra Cabaña & Argimiro Arratia & Pere Puig, 2020. "Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0242956
    DOI: 10.1371/journal.pone.0242956
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    References listed on IDEAS

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    1. Winkelmann, Rainer, 1996. "Markov Chain Monte Carlo Analysis of Underreported Count Data with an Application to Worker Absenteeism," Empirical Economics, Springer, vol. 21(4), pages 575-587.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > SARS

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    Cited by:

    1. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).

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