IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v136y2020ics0960077920303295.html
   My bibliography  Save this article

COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?

Author

Listed:
  • Crokidakis, Nuno

Abstract

The recent Coronavirus has been spreading through all the world fastly. In this work we focus on the evolution of the COVID-19 in one of the most populous Brazilian states, namely the Rio de Janeiro state. The first case was reported in March 5, 2020, thus we have a considerable amount of available data to make a good analysis. We study the dynamics of COVID-19 through a Susceptible-Infectious-Quarantined-Recovered (SIQR) model with an additional mechanism that represents the implementation of public policies of social isolation. Based on the data collected from the Rio de Janeiro state Department of Health from March 5, 2020 through April 26, 2020, we observed that the implementation of social distancing policies changed the initial exponential growth to a sub-exponential one. The SIQR model with the above-mentioned mechanism is capable of reproducing the data of confirmed cases in Rio de Janeiro, and it explains how that change occurred. The model also predicts an important mitigation effect, namely the flattening effect, i.e., the considerably decrease of the maximum number of confirmed cases. Through the results of the model, this effect can be directly related to the social isolation policies. Finally, we consider the relaxation of such policies, and discuss about the ideal period of time to release people to return to their activities.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920303295
    DOI: 10.1016/j.chaos.2020.109930
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920303295
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109930?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. 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).
    3. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Mandal, Manotosh & Jana, Soovoojeet & Nandi, Swapan Kumar & Khatua, Anupam & Adak, Sayani & Kar, T.K., 2020. "A model based study on the dynamics of COVID-19: Prediction and control," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    6. Abdo, Mohammed S. & Shah, Kamal & Wahash, Hanan A. & Panchal, Satish K., 2020. "On a comprehensive model of the novel coronavirus (COVID-19) under Mittag-Leffler derivative," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zhang, Ge & Li, Zhiming & Din, Anwarud & Chen, Tao, 2024. "Dynamic analysis and optimal control of a stochastic COVID-19 model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 498-517.
    2. Okuonghae, D. & Omame, A., 2020. "Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Moon-Hyun Kim & Jiwon Lee & Hee-Jin Oh & Tsolmon Bayarsaikhan & Tae-Hyoung Tommy Gim, 2023. "A modeling study of the effect of social distancing policies on the early spread of coronavirus disease 2019: a case of South Korea," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(1), pages 225-242, August.
    4. Crokidakis, Nuno & Sigaud, Lucas, 2021. "Modeling the evolution of drinking behavior: A Statistical Physics perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    5. 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).
    6. de Souza, Silvio L.T. & Batista, Antonio M. & Caldas, Iberê L. & Iarosz, Kelly C. & Szezech Jr, José D., 2021. "Dynamics of epidemics: Impact of easing restrictions and control of infection spread," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. Castillo, Oscar & Melin, Patricia, 2020. "Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Walter Leal Filho & Amanda Lange Salvia & Arminda Paço & Celia Dias-Ferreira & Samara Neiva & Izabela Simon Rampasso & Rosley Anholon & Claudio Ruy Portela de Vasconcelos & João Henrique Paulino Pires, 2022. "Assessing the Connections between COVID-19 and Waste Management in Brazil," Sustainability, MDPI, vol. 14(13), pages 1-19, July.
    9. Castillo, Oscar & Melin, Patricia, 2021. "A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Rafael Pérez Abreu C. & Samantha Estrada & Héctor de-la-Torre-Gutiérrez, 2021. "A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    4. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. 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).
    8. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    9. Aldila, Dipo & Khoshnaw, Sarbaz H.A. & Safitri, Egi & Anwar, Yusril Rais & Bakry, Aanisah R.Q. & Samiadji, Brenda M. & Anugerah, Demas A. & GH, M. Farhan Alfarizi & Ayulani, Indri D. & Salim, Sheryl N, 2020. "A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. 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.
    11. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    12. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    13. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    14. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    15. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    16. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    17. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    18. Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    19. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    20. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    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:eee:chsofr:v:136:y:2020:i:c:s0960077920303295. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.