Open Access

ARTICLE

A Fractional-Order Study for Bicomplex Haemorrhagic Infection in Several Populations Conditions

Muhammad Farman^1,2,3,*, Muhammad Hashir Zubair⁴, Hua Li⁴, Kottakkaran Sooppy Nisar^5,6, Mohamad Hafez^7,8

1 Faculty of Art and Sciences, Department of Mathematics, Near East University, Nicosia, 99010, Türkiye
2 Research Center of Applied Mathematics, Khazar University, Baku, AZ1096, Azerbaijan
3 Jadara University Research Center, Jadara University, Irbid, 21110, Jordan
4 School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, 450001, China
5 Department of Mathematics College of Arts and Science, Prince Sattam bin Abdulaziz University, Alkharj, 16273, Saudi Arabia
6 Department of Mathematical Sciences, Saveetha School of Engineering, SIMATS, Chennai, 602105, Tamilnadu, India
7 Faculty of Engineering and Quantity Surviving, INTI International University Colleges, Nilai, 71800, Malaysia
8 Faculty of Management, Shinawatra University, Pathum Thani, 12160, Thailand

* Corresponding Author: Muhammad Farman. Email:

(This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)

Computer Modeling in Engineering & Sciences 2026, 146(1), 30 https://doi.org/10.32604/cmes.2025.074160

Received 04 October 2025; Accepted 13 November 2025; Issue published 29 January 2026

Abstract

Lassa Fever (LF) is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa, causing thousands of deaths annually. This study develops a dynamic model of Lassa virus transmission, capturing the progression of the disease through susceptible, exposed, infected, and recovered populations. The focus is on simulating this model using the fractional Caputo derivative, allowing both qualitative and quantitative analyses of boundedness, positivity, and solution uniqueness. Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions, while Lyapunov functions establish the global stability of both disease-free and endemic equilibria. The study further examines the role of the Caputo operator by solving the generalized power-law kernel via a two-step Lagrange polynomial method. This approach offers practical advantages in handling additional data points in integral forms, though Newton polynomial-based schemes are generally more accurate and can outperform Lagrange-based Adams-Bashforth methods. Graphical simulations validate the proposed numerical approach for different fractional orders (ν) and illustrate the influence of model parameters on disease dynamics. Results indicate that increasing the fractional order accelerates the decline of Lassa fever in both human and rodent populations. Moreover, fractional-order modeling provides more nuanced insights than traditional integer-order models, suggesting potential improvements for medical intervention strategies. The study demonstrates that carefully chosen fractional orders can optimize convergence and enhance the predictive capacity of Lassa fever models, offering a promising direction for future research in epidemiological modeling.

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