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How relevant is the decision of containment measures against COVID-19 applied ahead of time?

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  • Brugnago, Eduardo L.
  • da Silva, Rafael M.
  • Manchein, Cesar
  • Beims, Marcus W.

Abstract

The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30th, 2020, is presented for the countries: Belgium, Brazil, United Kingdom (UK), and the United States of America (USA). After an initial period with a low incidence of newly infected people, a power-law growth of the number of confirmed cases is observed. For each country, a distinct growth exponent is obtained. For Belgium, UK, and USA, countries with a large number of infected people, after the power-law growth, a distinct behavior is obtained when approaching saturation. Brazil is still in the power-law regime. Such updates of the data and projections corroborate recent results regarding the power-law growth of the virus and their strong Distance Correlation between some countries around the world. Furthermore, we show that act in time is one of the most relevant non-pharmacological weapons that the health organizations have in the battle against the COVID-19, infectious disease caused by the most recently discovered coronavirus. We study how changing the social distance and the number of daily tests to identify infected asymptomatic individuals can interfere in the number of confirmed cases of COVID-19 when applied in three distinct days, namely April 16th (early), April 30th (current), and May 14th (late). Results show that containment actions are necessary to flatten the curves and should be applied as soon as possible.

Suggested Citation

  • Brugnago, Eduardo L. & da Silva, Rafael M. & Manchein, Cesar & Beims, Marcus W., 2020. "How relevant is the decision of containment measures against COVID-19 applied ahead of time?," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305609
    DOI: 10.1016/j.chaos.2020.110164
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    References listed on IDEAS

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    1. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    2. Glenn Ellison, 2020. "Implications of Heterogeneous SIR Models for Analyses of COVID-19," NBER Working Papers 27373, National Bureau of Economic Research, Inc.
    3. Mendes, Carlos F.O. & Beims, Marcus W., 2018. "Distance correlation detecting Lyapunov instabilities, noise-induced escape times and mixing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 721-730.
    4. Hattaf, Khalid & Dutta, Hemen, 2020. "Modeling the dynamics of viral infections in presence of latently infected cells," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
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    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Olivares, Alberto & Staffetti, Ernesto, 2023. "A statistical moment-based spectral approach to the chance-constrained stochastic optimal control of epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Alberto Olivares & Ernesto Staffetti, 2021. "Optimal Control Applied to Vaccination and Testing Policies for COVID-19," Mathematics, MDPI, vol. 9(23), pages 1-22, December.
    4. Olivares, Alberto & Staffetti, Ernesto, 2021. "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Sharov, Konstantin S., 2020. "Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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