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Differential Equations and New Methods in Mathematical Biology

Submission Deadline: 31 January 2023 (closed)

Guest Editors

Professor Şuayip Yüzbaşı, Akdeniz University, Turkey
Professor Saeid Abbasbandy, Imam Khomeini International University, Iran
Dr. Harendra Singh, Post Graduate College Ghazipur U. (V. B. S. Purvanchal University), India

Summary

This special issue will focus on differential equations and new methods in Mathematical Biology. Differential equations are used as a tool in modeling many model problems in Science and Engineering. Often, computing analytical solutions to these equations is difficult or impossible. Therefore, numerical methods are indeed. Also, in addition to obtaining the numerical solutions of these equations, it is very important that the methods to be used are effective and practical, too. So, this special issue is to aim the development and analysis of new numerical methods for the problems modeled with differential equations in Computational Biology. On the other hand, analysis of new models characterized by differential equations in computational biology is also included in this special issue.


This special issue will contribute to new models characterized by differential equations in computational biology and new methods for them. We would like to invite researchers working on this topic to submit their articles to this Special Issue of Differential Equations and New Methods in Mathematical Biology.


Keywords

•Numerical methods for nonlinear/fractional differential equations with applications in Biology
•Fractional calculus in Computational Biology
•Numerical Methods for COVID-19
•Numerical Methods for HIV/AIDS infection
•Numerical Methods for population models
•Numerical methods for Epidemic models
•Cancer dynamical systems
•New models characterized by differential equations in Computational Biology
•Stability analysis of new models in Computational Biology
•Dynamical behavior of new models in Mathematical Biology

Published Papers


  • Open Access

    ARTICLE

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

    Mohammad Aslefallah, Şuayip Yüzbaşi, Saeid Abbasbandy
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1687-1706, 2023, DOI:10.32604/cmes.2023.025647
    (This article belongs to this Special Issue: Differential Equations and New Methods in Mathematical Biology)
    Abstract In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error analysis of the method, the… More >

    Graphic Abstract

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

  • Open Access

    ARTICLE

    The Influence of Saturated and Bilinear Incidence Functions on the Dynamical Behavior of HIV Model Using Galerkin Scheme Having a Polynomial of Order Two

    Attaullah, Kamil Zeb, Abdullah Mohamed
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1661-1685, 2023, DOI:10.32604/cmes.2023.023059
    (This article belongs to this Special Issue: Differential Equations and New Methods in Mathematical Biology)
    Abstract Mathematical modelling has been extensively used to measure intervention strategies for the control of contagious conditions. Alignment between different models is pivotal for furnishing strong substantiation for policymakers because the differences in model features can impact their prognostications. Mathematical modelling has been widely used in order to better understand the transmission, treatment, and prevention of infectious diseases. Herein, we study the dynamics of a human immunodeficiency virus (HIV) infection model with four variables: S (t), I (t), C (t), and A (t) the susceptible individuals; HIV infected individuals (with no clinical symptoms of AIDS); HIV infected individuals (under ART with… More >

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