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

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Article number660
JournalFractal and Fractional
Issue number9
Publication statusPublished - Sept 2023


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

ASJC Scopus subject areas

  • Analysis
  • Statistical and Nonlinear Physics
  • Statistics and Probability


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