IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-020-19798-3.html
   My bibliography  Save this article

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Author

Listed:
  • Juliane F. Oliveira

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
    Department of Mathematics)

  • Daniel C. P. Jorge

    (Universidade Federal da Bahia)

  • Rafael V. Veiga

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz)

  • Moreno S. Rodrigues

    (Fundação Oswaldo Cruz, Porto Velho)

  • Matheus F. Torquato

    (Swansea University)

  • Nivea B. Silva

    (Universidade Federal da Bahia)

  • Rosemeire L. Fiaccone

    (Universidade Federal da Bahia)

  • Luciana L. Cardim

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz)

  • Felipe A. C. Pereira

    (Universidade de São Paulo)

  • Caio P. Castro

    (Universidade Federal da Bahia)

  • Aureliano S. S. Paiva

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz)

  • Alan A. S. Amad

    (Swansea University)

  • Ernesto A. B. F. Lima

    (The University of Texas at Austin)

  • Diego S. Souza

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz)

  • Suani T. R. Pinho

    (Universidade Federal da Bahia)

  • Pablo Ivan P. Ramos

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz)

  • Roberto F. S. Andrade

    (Instituto Gonçalo Moniz, Fundação Oswaldo Cruz
    Universidade Federal da Bahia)

Abstract

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.

Suggested Citation

  • Juliane F. Oliveira & Daniel C. P. Jorge & Rafael V. Veiga & Moreno S. Rodrigues & Matheus F. Torquato & Nivea B. Silva & Rosemeire L. Fiaccone & Luciana L. Cardim & Felipe A. C. Pereira & Caio P. Cas, 2021. "Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-19798-3
    DOI: 10.1038/s41467-020-19798-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-19798-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-19798-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    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:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-19798-3. 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.nature.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.