Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process
In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the proposed process a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/7/9/660 |
| _version_ | 1797580010803953664 |
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| author | Ahmed Nafidi Yassine Chakroune Ramón Gutiérrez-Sánchez Abdessamad Tridane |
| author_facet | Ahmed Nafidi Yassine Chakroune Ramón Gutiérrez-Sánchez Abdessamad Tridane |
| author_sort | Ahmed Nafidi |
| collection | DOAJ |
| description | In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the proposed process are obtained. Next, the parameters of the model are estimated by the maximum likelihood methodology. This estimation and the subsequent statistical inference are based on the discrete observation of the variable <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>x</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula> “number of active cases of COVID-19 in Morocco” by using the data for the period of 28 January to 4 March 2022. Then, we analyze the mean functions by using simulated data for fit and forecast purposes. Finally, we explore the illustration of using this new process to fit and forecast the active cases of COVID-19 data. |
| first_indexed | 2024-03-10T22:44:16Z |
| format | Article |
| id | doaj.art-581bf4c83eef4571a6528e074a9c1653 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-10T22:44:16Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-581bf4c83eef4571a6528e074a9c16532023-11-19T10:48:25ZengMDPI AGFractal and Fractional2504-31102023-08-017966010.3390/fractalfract7090660Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion ProcessAhmed Nafidi0Yassine Chakroune1Ramón Gutiérrez-Sánchez2Abdessamad Tridane3Laboratory of Systems Modelization and Analysis for Decision Support, Department of Mathematics and Computer Science, National School of Applied Science, Hassan First University of Settat, B.P. 218, 26103 Berrechid, MoroccoLaboratory of Systems Modelization and Analysis for Decision Support, Department of Mathematics and Computer Science, National School of Applied Science, Hassan First University of Settat, B.P. 218, 26103 Berrechid, MoroccoDepartment of Statistics and Operational Research, Facultad de Ciencias, Compus Fuente Nueva de University of Granada, 18071 Granada, SpainDepartment of Mathematical Sciences, College of Science, United Arab Emirates University, Al Ain 15551, United Arab EmiratesIn this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics and analytic expression of the proposed process are obtained. Next, the parameters of the model are estimated by the maximum likelihood methodology. This estimation and the subsequent statistical inference are based on the discrete observation of the variable <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>x</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula> “number of active cases of COVID-19 in Morocco” by using the data for the period of 28 January to 4 March 2022. Then, we analyze the mean functions by using simulated data for fit and forecast purposes. Finally, we explore the illustration of using this new process to fit and forecast the active cases of COVID-19 data.https://www.mdpi.com/2504-3110/7/9/660Rayleigh distributiondiffusion process estimationmean functionsimulated annealingCOVID-19 |
| spellingShingle | Ahmed Nafidi Yassine Chakroune Ramón Gutiérrez-Sánchez Abdessamad Tridane Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process Fractal and Fractional Rayleigh distribution diffusion process estimation mean function simulated annealing COVID-19 |
| title | Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process |
| title_full | Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process |
| title_fullStr | Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process |
| title_full_unstemmed | Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process |
| title_short | Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process |
| title_sort | forecasting the active cases of covid 19 via a new stochastic rayleigh diffusion process |
| topic | Rayleigh distribution diffusion process estimation mean function simulated annealing COVID-19 |
| url | https://www.mdpi.com/2504-3110/7/9/660 |
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