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    Dynamic Modeling and Identification of the COVID-19 Stochastic Dispersion

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    Author
    ElKafrawy, Passent cc
    Gamal, Mohamed Taher
    Hedaya, Mohammed M.
    Bakeer, Bahi
    Zakaria, Mahmoud
    Subject
    COVID-19 , Temperature
    Computational modeling ,
    Stochastic processes , Predictive models , Data models ,
    Data Mining
    Date
    2023-01
    
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    Abstract
    In this work, the stochastic dispersion of novel coronavirus disease 2019 (COVID-19) at the borders between France and Italy has been considered using a multi-input multi-output stochastic model. The physical effects of wind, temperature and altitude have been investigated as these factors and physical relationships are stochastic in nature. Stochastic terms have also been included to take into account the turbulence effect, and the random nature of the above physical parameters considered. Then, a method is proposed to identify the developed model's order and parameters. The actual data has been used in the identification and prediction process as a reference. These data have been divided into two parts: the first part is used to calculate the stochastic parameters of the model which are used to predict the COVID-19 level, while the second part is used as a check data. The predicted results are in good agreement with the check data.
    Department
    Computer Science
    Publisher
    IEEE
    DOI
    10.1109/ESOLEC54569.2022.10009467
    ae974a485f413a2113503eed53cd6c53
    10.1109/ESOLEC54569.2022.10009467
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