Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process Nafidi, Ahmed Chakroune, Yassine Gutiérrez Sánchez, Ramón Tridane, Abdessamad Rayleigh distribution Diffusion process estimation Mean function 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 x(t) “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. 2024-09-23T07:40:42Z 2024-09-23T07:40:42Z 2023-08-31 journal article Nafidi, A.; Chakroune, Y.; Gutiérrez-Sánchez, R.; Tridane, A. Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process. Fractal Fract. 2023, 7, 660. https://doi.org/10.3390/fractalfract7090660 https://hdl.handle.net/10481/94832 10.3390/fractalfract7090660 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI