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Estimate of underreporting of COVID-19 in Brazil by Acute Respiratory Syndrome hospitalization reports

Author

Listed:
  • Leonardo Costa Ribeiro

    (Cedeplar-UFMG)

  • Américo Tristão Bernardes

    (UFOP)

Abstract

The number of COVID-19 infected people in each country is a crucial factor to determine public policies. It guides the governments to strengthen movement restrictions of people or to relieve it. The number of infected people is very important to forecast the needs of the health systems, which are collapsing in many countries. Thus, underreporting of infected people is a huge problem, since authorities do not know the real problem and act in darkness. In the present work, we discuss this subject for the Brazilian case. We take the time series of acute respiratory syndromes reported in the health public system in the last ten years and estimated the number for March/20 when the COVID-19 appeared in Brazil. Our results show a 7.7:1 rate of underreporting, meaning that the real cases in Brazil should be, at least, seven times the publicized number.

Suggested Citation

  • Leonardo Costa Ribeiro & Américo Tristão Bernardes, 2020. "Estimate of underreporting of COVID-19 in Brazil by Acute Respiratory Syndrome hospitalization reports," Notas Técnicas Cedeplar-UFMG 010, Cedeplar, Universidade Federal de Minas Gerais.
    • Handle: RePEc:cdp:tecnot:tn010
    as

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    File URL: https://www.cedeplar.ufmg.br/component/phocadownload/category/18-noticias?download=1296:subestimacao-covid19
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    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

    Citations

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    Cited by:

    1. Jinjie Chen & Joon Jin Song & James D. Stamey, 2022. "A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    2. M. R. Martines & R. V. Ferreira & R. H. Toppa & L. M. Assunção & M. R. Desjardins & E. M. Delmelle, 2021. "Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities," Journal of Geographical Systems, Springer, vol. 23(1), pages 7-36, January.
    3. Menton, Mary & Milanez, Felipe & Souza, Jurema Machado de Andrade & Cruz, Felipe Sotto Maior, 2021. "The COVID-19 pandemic intensified resource conflicts and indigenous resistance in Brazil," World Development, Elsevier, vol. 138(C).
    4. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    More about this item

    Keywords

    Corona virus; COVID-19; Underreporting; Brazil;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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