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A Study on the Nonlinear Caputo-Type Snakebite Envenoming Model with Memory

Pushpendra Kumar1,*, Vedat Suat Erturk2, V. Govindaraj1, Dumitru Baleanu3,4,5

1 Department of Mathematics, National Institute of Technology Puducherry, Karaikal, 609609, India
2 Department of Mathematics, Faculty of Arts and Sciences, Ondokuz Mayis University, Atakum, Samsun, 55200, Turkey
3 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey
4 Institute of Space Sciences, Magurele, Bucharest, R76900, Romania
5 Department of Computer Science and Mathematics, Lebanese American University, Beirut, 11022801, Lebanon

* Corresponding Author: Pushpendra Kumar. Email:

(This article belongs to this Special Issue: Advanced Numerical Methods for Fractional Differential Equations)

Computer Modeling in Engineering & Sciences 2023, 136(3), 2487-2506.


In this article, we introduce a nonlinear Caputo-type snakebite envenoming model with memory. The well-known Caputo fractional derivative is used to generalize the previously presented integer-order model into a fractional-order sense. The numerical solution of the model is derived from a novel implementation of a finite-difference predictor-corrector (L1-PC) scheme with error estimation and stability analysis. The proof of the existence and positivity of the solution is given by using the fixed point theory. From the necessary simulations, we justify that the first-time implementation of the proposed method on an epidemic model shows that the scheme is fully suitable and time-efficient for solving epidemic models. This work aims to show the novel application of the given scheme as well as to check how the proposed snakebite envenoming model behaves in the presence of the Caputo fractional derivative, including memory effects.


Cite This Article

Kumar, P., Erturk, V. S., Govindaraj, V., Baleanu, D. (2023). A Study on the Nonlinear Caputo-Type Snakebite Envenoming Model with Memory. CMES-Computer Modeling in Engineering & Sciences, 136(3), 2487–2506.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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