Olaniyi Samuel Iyiola

Prof. Olaniyi Samuel Iyiola

Department of Mathematics, Computer Science and Information Systems, California University of Pennsylvania, PA 15419, USA
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Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 January 2022 in Journal of Scientific Computing
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YYYonghong Yao
Yonghong Yao
Olaniyi Samuel IyiolaOlaniyi Samuel Iyiola
Olaniyi Samuel Iyiola
Yekini ShehuYekini Shehu
Yekini Shehu

26 April 2020 in Mathematical Methods in the Applied Sciences
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LALanre Akinyemi
Lanre Akinyemi
Olaniyi Samuel IyiolaOlaniyi Samuel Iyiola
Olaniyi Samuel Iyiola

23 January 2021 in Optimization and Engineering
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Yekini ShehuYekini Shehu
Yekini Shehu
Olaniyi Samuel IyiolaOlaniyi Samuel Iyiola
Olaniyi Samuel Iyiola
Simeon ReichSimeon Reich
Simeon Reich

9 November 2019 in Advances in Difference Equations
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MSMehmet Senol
Mehmet Senol
Olaniyi Samuel IyiolaOlaniyi Samuel Iyiola
Olaniyi Samuel Iyiola
Hamed Daei KasmaeiHamed Daei Kasmaei
Hamed Daei Kasmaei
LALanre Akinyemi
Lanre Akinyemi
In this paper, we present analytical-approximate solution to the time-fractional nonlinear coupled Jaulent–Miodek system of equations which comes with an energy-dependent Schrödinger potential by means of a residual power series method (RSPM) and a q-homotopy analysis method (q-HAM). These methods produce convergent series solutions with easily computable components. Using a specific example, a comparison analysis is done between these methods and the exact solution. The numerical results show that present methods are competitive, powerful, reliable, and easy to implement for strongly nonlinear fractional differential equations.

3 December 2020 in Advances in Difference Equations
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IOIsaac Owusu-Mensah
Isaac Owusu-Mensah
Lanre AkinyemiLanre Akinyemi
Lanre Akinyemi
BOBismark Oduro
Bismark Oduro
Olaniyi Samuel IyiolaOlaniyi Samuel Iyiola
Olaniyi Samuel Iyiola
The novel coronavirus (SARS-CoV-2), or COVID-19, has emerged and spread at fast speed globally; the disease has become an unprecedented threat to public health worldwide. It is one of the greatest public health challenges in modern times, with no proven cure or vaccine. In this paper, our focus is on a fractional order approach to modeling and simulations of the novel COVID-19. We introduce a fractional type susceptible–exposed–infected–recovered (SEIR) model to gain insight into the ongoing pandemic. Our proposed model incorporates transmission rate, testing rates, and transition rate (from asymptomatic to symptomatic population groups) for a holistic study of the coronavirus disease. The impacts of these parameters on the dynamics of the solution profiles for the disease are simulated and discussed in detail. Furthermore, across all the different parameters, the effects of the fractional order derivative are also simulated and discussed in detail. Various simulations carried out enable us gain deep insights into the dynamics of the spread of COVID-19. The simulation results confirm that fractional calculus is an appropriate tool in modeling the spread of a complex infectious disease such as the novel COVID-19. In the absence of vaccine and treatment, our analysis strongly supports the significance reduction in the transmission rate as a valuable strategy to curb the spread of the virus. Our results suggest that tracing and moving testing up has an important benefit. It reduces the number of infected individuals in the general public and thereby reduces the spread of the pandemic. Once the infected individuals are identified and isolated, the interaction between susceptible and infected individuals diminishes and transmission reduces. Furthermore, aggressive testing is also highly recommended.