@misc{10481/94832,
year = {2023},
month = {8},
url = {https://hdl.handle.net/10481/94832},
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 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.},
organization = {UAEU UPAR, grant number 12S125},
publisher = {MDPI},
keywords = {Rayleigh distribution},
keywords = {Diffusion process estimation},
keywords = {Mean function},
title = {Forecasting the Active Cases of COVID-19 via a New Stochastic Rayleigh Diffusion Process},
doi = {10.3390/fractalfract7090660},
author = {Nafidi, Ahmed and Chakroune, Yassine and Gutiérrez Sánchez, Ramón and Tridane, Abdessamad},
}