Fractional-Order Dynamics in Epidemic Disease Modeling with Advanced Perspectives of Fractional Calculus

被引:0
|
作者
Riaz, Muhammad [1 ]
Khan, Zareen A. [2 ]
Ahmad, Sadique [3 ]
Ateya, Abdelhamied Ashraf [3 ,4 ]
机构
[1] Univ Malakand, Dept Math, Chakdara 18000, Khyber Pakhtunk, Pakistan
[2] Princess Nourah Bint Abdulrahman Univ, Coll Sci, Dept Math Sci, POB 84428, Riyadh 11671, Saudi Arabia
[3] Prince Sultan Univ, Coll Comp & Informat Sci, EIAS Data Sci & Blockchain Lab, Riyadh 11586, Saudi Arabia
[4] Zagazig Univ, Dept Elect & Commun Engn, Zagazig 44519, Egypt
关键词
epidemic modeling; piecewise fractional-order derivative; numerical solutions; A-PRIORI PATHOMETRY; PROBABILITIES;
D O I
10.3390/fractalfract8050291
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Piecewise fractional-order differential operators have received more attention in recent years because they can be used to describe various evolutionary dynamical problems to investigate crossover behaviors. In this manuscript, we use the aforementioned operators to investigate a mathematical model of COVID-19. By utilizing fractional calculus, our approach aims to capture the crossover dynamics of disease spread, considering heterogeneity and transitions between epidemic phases. This research seeks to develop a framework using specialized mathematical techniques, such as the Caputo fractional derivative, with the potential to investigate the crossover dynamical behaviors of the considered epidemic model. The anticipated contribution lies in bridging fractional calculus and epidemiology, offering insights for both theoretical advancements and practical public health interventions. In order to improve our understanding of epidemic dynamics and support, we used MATLAB to simulate numerical results for a visual representation of our findings. For this interpretation, we used various fractional-order values. In addition, we also compare our simulated results with some reported results for infected and death classes to demonstrate the efficiency of our numerical method.
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页数:23
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