The dynamics of COVID-19 in the UAE based on fractional derivative modeling using Riesz wavelets simulation

Area of Science:

• Epidemiology
• Mathematical Modeling
• Computational Science

Background:

• The novel coronavirus (COVID-19) emerged as a global epidemic, causing significant mortality and widespread impact.
• Accurate prediction of COVID-19 transmission is crucial for public health interventions.
• The United Arab Emirates (UAE) reported substantial COVID-19 cases and deaths by May 2020.

Purpose of the Study:

• To develop and analyze a dynamical model for COVID-19 outbreak prediction.
• To simulate virus spread using fractional calculus and advanced numerical methods.
• To contribute to strategies for mitigating the COVID-19 pandemic.

Main Methods:

• Utilized a dynamical model based on fractional derivatives of nonlinear equations.
• Employed Riesz wavelets, specifically smoothed pseudosplines, for simulating reported infection data.
• Formulated the pandemic model using the Caputo fractional derivative and solved it numerically via a collocation Riesz wavelet system.

Main Results:

• Presented numerical solutions for the COVID-19 dynamics model.
• Illustrated model parameter variations under different scenarios through graphical representations.
• Provided a framework for understanding and potentially controlling virus spread.

Conclusions:

• The study offers a robust mathematical framework for analyzing COVID-19 transmission dynamics.
• Numerical simulations based on fractional calculus and wavelets provide insights into pandemic behavior.
• The results are expected to support ongoing efforts to reduce virus spread and infection rates.