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Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach

Author

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  • Barmparis, G.D.
  • Tsironis, G.P.

Abstract

The COVID-19 pandemic has affected all countries of the world producing a substantial number of fatalities accompanied by a major disruption in their social, financial and educational organization. The strict disciplinary measures implemented by China were very effective and thus were subsequently adopted by most world countries to various degrees. The infection duration and number of infected persons are of critical importance for the battle against the pandemic. We use the quantitative landscape of the disease spreading in China as a benchmark and utilize infection data from eight countries to estimate the complete evolution of the infection in each of these countries. The analysis predicts successfully both the expected number of daily infections per country and, perhaps more importantly, the duration of the epidemic in each country. Our quantitative approach is based on a Gaussian spreading hypothesis that is shown to arise as a result of imposed measures in a simple dynamical infection model. This may have consequences and shed light in the efficiency of policies once the phenomenon is over.

Suggested Citation

  • Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920302423
    DOI: 10.1016/j.chaos.2020.109842
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    Citations

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    Cited by:

    1. Victor Zakharov & Yulia Balykina & Ovanes Petrosian & Hongwei Gao, 2020. "CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time," Mathematics, MDPI, vol. 8(10), pages 1-10, October.
    2. Mudassar Arsalan & Omar Mubin & Fady Alnajjar & Belal Alsinglawi, 2020. "COVID-19 Global Risk: Expectation vs. Reality," IJERPH, MDPI, vol. 17(15), pages 1-10, August.
    3. José M. Garrido & David Martínez-Rodríguez & Fernando Rodríguez-Serrano & Sorina-M. Sferle & Rafael-J. Villanueva, 2021. "Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months," Mathematics, MDPI, vol. 9(10), pages 1-21, May.
    4. Alaeddine Mihoub & Hosni Snoun & Moez Krichen & Montassar Kahia & Riadh Bel Hadj Salah, 2020. "Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques," Post-Print hal-03002886, HAL.
    5. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    6. Kaxiras, Efthimios & Neofotistos, Georgios & Angelaki, Eleni, 2020. "The first 100 days: Modeling the evolution of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. 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).
    8. Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Păcurar, Cristina-Maria & Necula, Bogdan-Radu, 2020. "An analysis of COVID-19 spread based on fractal interpolation and fractal dimension," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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