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    Predicting COVID-19 in very large countries: The case of Brazil

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    Predicting COVID-19 in very large countries - The case of Brazil.pdf (11.26Mb)
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    Artigo de Periódico
    Date
    2021
    Author
    Parro, Vanderlei Cunha
    Lafetá, M.L.M.
    Pait, Felipe Miguel
    Ipólito, F.B.
    Toporcov, Tatiana Natasha
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    Abstract
    This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases. Copyright: © 2021 Parro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    1. Brazil
    2. COVID-19
    3. Delivery of Health Care
    4. Humans
    5. Machine Learning
    6. Models, Statistical
    7. SARS-CoV-2
    8. pioglitazone
    9. Aedes aegypti
    10. Article
    11. basic reproduction number
    12. Brazil
    13. coronavirus disease 2019
    14. disease surveillance
    15. disease transmission
    16. epidemic
    17. health care facility
    18. health care system
    19. human
    20. machine learning
    21. mathematical model
    22. nonhuman
    23. population size
    24. prediction
    25. process optimization
    26. risk assessment
    27. seasonal variation
    28. seroconversion
    29. solid waste management
    30. tonic clonic seizure
    31. vaccination
    32. Zika fever
    33. Zika virus
    34. epidemiology
    35. health care delivery
    36. isolation and purification
    37. statistical model
    URI
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108988349&doi=10.1371%2fjournal.pone.0253146&partnerID=40&md5=ca674f2d479ad31e96c57095f3932566
    https://repositorio.maua.br/handle/MAUA/1389
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