Assessing the global dynamics of Nipah infection under vaccination and treatment: A novel computational modeling approach

PLoS One. 2025 Jan 14;20(1):e0309360. doi: 10.1371/journal.pone.0309360. eCollection 2025.

Abstract

In biology and life sciences, fractal theory and fractional calculus have significant applications in simulating and understanding complex problems. In this paper, a compartmental model employing Caputo-type fractional and fractal-fractional operators is presented to analyze Nipah virus (NiV) dynamics and transmission. Initially, the model includes nine nonlinear ordinary differential equations that consider viral concentration, flying fox, and human populations simultaneously. The model is reconstructed using fractional calculus and fractal theory to better understand NiV transmission dynamics. We analyze the model's existence and uniqueness in both contexts and instigate the equilibrium points. The clinical epidemiology of Bangladesh is used to estimate model parameters. The fractional model's stability is examined using Ulam-Hyers and Ulam-Hyers-Rassias stabilities. Moreover, interpolation methods are used to construct computational techniques to simulate the NiV model in fractional and fractal-fractional cases. Simulations are performed to validate the stable behavior of the model for different fractal and fractional orders. The present findings will be beneficial in employing advanced computational approaches in modeling and control of NiV outbreaks.

MeSH terms

  • Animals
  • Bangladesh / epidemiology
  • Computer Simulation*
  • Disease Outbreaks / prevention & control
  • Fractals
  • Henipavirus Infections* / epidemiology
  • Henipavirus Infections* / prevention & control
  • Henipavirus Infections* / transmission
  • Humans
  • Nipah Virus*
  • Vaccination*

Grants and funding

This article has been produced with the financial support of the European Union under the REFRESH – Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition. Also, this work is supported by the Natural Science Foundation of Xinjiang, China (2021D01C003).