IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-33050-6_20.html
   My bibliography  Save this book chapter

Analysis of Covid-19 Dynamics in Brazil by Recursive State and Parameter Estimations

In: Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

Author

Listed:
  • Daniel Martins Silva

    (Center of Technology, COPPE, Federal University of Rio de Janeiro)

  • Argimiro Resende Secchi

    (Center of Technology, COPPE, Federal University of Rio de Janeiro)

Abstract

In this article, the epidemiological dynamics of COVID-19 in Brazil were studied for each federative unit using state and parameter estimations based on public data. Transmissibility, the severity of the disease, and mortality dynamics were estimated over time for each case study through augmented states using a constrained extended Kalman filter (CEKF) and a Rauch-Tung-Striebel smoother. The estimated parameters were defined in a proposed SEIR-based model containing mobility data and heuristic assumptions. In addition, a heuristic data preprocessing was performed for each case study to remove data outliers from underreporting and overreporting. Simulations evaluated the COVID-19 epidemiological dynamics for each case study from 1 October 2020 to 1 October 2022. The dynamic feedback approach was efficient in detecting emerging predominant variants in most federative units. In addition, it satisfactorily characterized the predominant variants circulating dynamics under time-varying model uncertainty. Nonetheless, the technique was insufficient to describe the case studies with meaningful underreporting changes or poor data quality over time.

Suggested Citation

  • Daniel Martins Silva & Argimiro Resende Secchi, 2023. "Analysis of Covid-19 Dynamics in Brazil by Recursive State and Parameter Estimations," Springer Books, in: Rubem P. Mondaini (ed.), Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics, pages 335-374, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-33050-6_20
    DOI: 10.1007/978-3-031-33050-6_20
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-33050-6_20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.