Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process

Ahmed Nafidi; Yassine Chakroune; Ramón Gutiérrez-Sánchez; Abdessamad Tridane

doi:10.3390/fractalfract7090660

Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process

Ahmed Nafidi, Yassine Chakroune, Ramón Gutiérrez-Sánchez, Abdessamad Tridane

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

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 (Formula presented.) “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.

Original language	English
Article number	660
Journal	Fractal and Fractional
Volume	7
Issue number	9
DOIs	https://doi.org/10.3390/fractalfract7090660
Publication status	Published - Sept 2023

Keywords

COVID-19
diffusion process estimation
mean function
Rayleigh distribution
simulated annealing

ASJC Scopus subject areas

Analysis
Statistical and Nonlinear Physics
Statistics and Probability

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10.3390/fractalfract7090660

Cite this

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title = "Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process",

abstract = "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 (Formula presented.) “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.",

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AU - Nafidi, Ahmed

AU - Chakroune, Yassine

AU - Gutiérrez-Sánchez, Ramón

AU - Tridane, Abdessamad

PY - 2023/9

Y1 - 2023/9

N2 - 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 (Formula presented.) “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.

AB - 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 (Formula presented.) “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.

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KW - simulated annealing

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Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process

Abstract

Keywords

ASJC Scopus subject areas

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