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

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  • معلومة اضافية
    • المصدر:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: San Francisco, CA : Public Library of Science
    • الموضوع:
      Models, Statistical*; COVID-19/*epidemiology ; Disease Notification/*statistics & numerical data ; Pandemics/*statistics & numerical data; Basic Reproduction Number ; COVID-19/economics ; COVID-19/transmission ; Cost of Illness ; Humans ; Likelihood Functions ; Markov Chains
    • نبذة مختصرة :
      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.
      Competing Interests: The authors have declared that no competing interests exist.
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    • الموضوع:
      Date Created: 20201203 Date Completed: 20201218 Latest Revision: 20240330
    • الموضوع:
      20240330
    • الرقم المعرف:
      PMC7714127
    • الرقم المعرف:
      10.1371/journal.pone.0242956
    • الرقم المعرف:
      33270713