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Parameter estimation and prediction for coronavirus disease outbreak 2019 (COVID-19) in Algeria

1 Department of Mathematics and Informatics, University center of Ain Temouchent, Algeria
2 Laboratoire d’Analyse Nonlinéaire et Mathématiques Appliquées, University of Tlemcen, Tlemcen 13000, Algeria
3 Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan

Special Issues: Coronavirus disease 2019: Modeling, Control and Prediction

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Background: The wave of the coronavirus disease outbreak in 2019 (COVID-19) has spread all over the world. In Algeria, the first case of COVID-19 was reported on 25 February, 2020, and the number of confirmed cases of it has increased day after day. To overcome this difficult period and a catastrophic scenario, a model-based prediction of the possible epidemic peak and size of COVID-19 in Algeria is required. Methods: We are concerned with a classical epidemic model of susceptible, exposed, infected and removed (SEIR) population dynamics. By using the method of least squares and the best fit curve that minimizes the sum of squared residuals, we estimate the epidemic parameter and the basic reproduction number ${\cal R}_0$. Moreover, we discuss the effect of intervention in a certain period by numerical simulation. Results: We find that ${\cal R}_0$= 4.1, which implies that the epidemic in Algeria could occur in a strong way. Moreover, we obtain the following epidemiological insights: the intervention has a positive effect on the time delay of the epidemic peak; the epidemic size is almost the same for a short intervention; a large epidemic can occur even if the intervention is long and sufficiently effective. Conclusion: Algeria must implement the strict measures as shown in this study, which could be similar to the one that China has finally adopted.
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Citation: Soufiane Bentout, Abdennasser Chekroun, Toshikazu Kuniya. Parameter estimation and prediction for coronavirus disease outbreak 2019 (COVID-19) in Algeria. AIMS Public Health , 2020, 7(2): 306-318. doi: 10.3934/publichealth.2020026

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