Browsing by Author "Fernandez Fontelo, Amanda"

Now showing items 1-4 of 4

    • Cumulated burden of Covid-19 in Spain from a Bayesian perspective 

      Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Puig, Pedro (2021-12)
      Article
      Open Access
      Background The main goal of this work is to estimate the actual number of cases of Covid-19 in Spain in the period 01-31-2020/06-01-2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately ...

    • Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series 

      Moriña Soler, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig Casado, Pere (2023-03-28)
      Article
      Open Access
      Background The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data ...

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

      Fernandez Fontelo, Amanda; Moriña, David; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig, Pedro (Public Library of Science (PLOS), 2020-12-03)
      Article
      Open Access
      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 ...

    • Misreported longitudinal data in epidemiology: review of mixture-based advances and current challenges 

      Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig Casado, Pere (2021-12)
      Article
      Open Access
      The problem of dealing with misreported data is very common in a wide range of contexts and for different reasons. This has been and still is an important issue for data analysts and statisticians as not accounting for it ...