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

Autor:	Ahmed Nafidi, Yassine Chakroune, Ramón Gutiérrez-Sánchez, Abdessamad Tridane
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Rayleigh distribution diffusion process estimation mean function simulated annealing COVID-19 Thermodynamics QC310.15-319 Mathematics QA1-939 Analysis QA299.6-433
Zdroj:	Fractal and Fractional, Vol 7, Iss 9, p 660 (2023)
Druh dokumentu:	article
ISSN:	2504-3110
DOI:	10.3390/fractalfract7090660
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/581bf4c83eef4571a6528e074a9c1653 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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