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Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil

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  • Reis, Ruy Freitas
  • de Melo Quintela, Bárbara
  • de Oliveira Campos, Joventino
  • Gomes, Johnny Moreira
  • Rocha, Bernardo Martins
  • Lobosco, Marcelo
  • Weber dos Santos, Rodrigo

Abstract

By April 7th, 2020, the Coronavirus disease 2019 (COVID-19) has infected one and a half million people worldwide, accounting for over 80 thousand of deaths in 209 countries and territories around the world. The new and fast dynamics of the pandemic are challenging the health systems of different countries. In the absence of vaccines or effective treatments, mitigation policies, such as social isolation and lock-down of cities, have been adopted, but the results vary among different countries. Some countries were able to control the disease at the moment, as is the case of South Korea. Others, like Italy, are now experiencing the peak of the pandemic. Finally, countries with emerging economies and social issues, like Brazil, are in the initial phase of the pandemic. In this work, we use mathematical models with time-dependent coefficients, techniques of inverse and forward uncertainty quantification, and sensitivity analysis to characterize essential aspects of the COVID-19 in the three countries mentioned above. The model parameters estimated for South Korea revealed effective social distancing and isolation policies, border control, and a high number in the percentage of reported cases. In contrast, underreporting of cases was estimated to be very high in Brazil and Italy. In addition, the model estimated a poor isolation policy at the moment in Brazil, with a reduction of contact around 40%, whereas Italy and South Korea estimated numbers for contact reduction are at 75% and 90%, respectively. This characterization of the COVID-19, in these different countries under different scenarios and phases of the pandemic, supports the importance of mitigation policies, such as social distancing. In addition, it raises serious concerns for socially and economically fragile countries, where underreporting poses additional challenges to the management of the COVID-19 pandemic by significantly increasing the uncertainties regarding its dynamics.

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  • Reis, Ruy Freitas & de Melo Quintela, Bárbara & de Oliveira Campos, Joventino & Gomes, Johnny Moreira & Rocha, Bernardo Martins & Lobosco, Marcelo & Weber dos Santos, Rodrigo, 2020. "Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
  • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302885
    DOI: 10.1016/j.chaos.2020.109888
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
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    Cited by:

    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Lee, Chaeyoung & Li, Yibao & Kim, Junseok, 2020. "The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Zhu, Cheng-Cheng & Zhu, Jiang, 2021. "Dynamic analysis of a delayed COVID-19 epidemic with home quarantine in temporal-spatial heterogeneous via global exponential attractor method," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Samir Haj Bloukh & Zehra Edis & Annis A. Shaikh & Habib M. Pathan, 2020. "A Look Behind the Scenes at COVID-19: National Strategies of Infection Control and Their Impact on Mortality," IJERPH, MDPI, vol. 17(15), pages 1-19, August.
    6. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    7. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    8. Yi-Tui Chen & Yung-Feng Yen & Shih-Heng Yu & Emily Chia-Yu Su, 2020. "An Examination on the Transmission of COVID-19 and the Effect of Response Strategies: A Comparative Analysis," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    9. Kumar Das, Dhiraj & Khatua, Anupam & Kar, T.K. & Jana, Soovoojeet, 2021. "The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India," Applied Mathematics and Computation, Elsevier, vol. 404(C).

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