Mathematical Modelling of Epidemic Dynamics and Control

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 679

Special Issue Editor


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Guest Editor
College of Science, Northwest A&F University, Yangling 712100, China
Interests: differential equations and dynamic systems; biomathematics
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Special Issue Information

Dear Colleagues,

As a Guest Editor, I am pleased to present this special issue on Mathematical Modelling of Epidemic Dynamics and Control. Infectious diseases remain a significant global challenge, and mathematical modeling plays a crucial role in understanding transmission mechanisms, evaluating intervention strategies, and guiding public health policies.

This collection explores cutting-edge research on epidemic modeling, including deterministic and stochastic frameworks, network-based approaches, and data-driven techniques. Key topics include:

  • Transmission dynamics of emerging and re-emerging infectious diseases.
  • Optimal control strategies, such as vaccination, quarantine, and social distancing.
  • Impact of human behavior and mobility on disease spread.
  • Machine learning and AI in epidemic forecasting.
  • Economic and social factors in epidemic management.

The contributions highlight both theoretical advances and practical applications, providing insights for policymakers and researchers. By integrating mathematical rigor with real-world data, this issue aims to enhance our ability to predict, prevent, and mitigate future outbreaks.

We hope this compilation fosters interdisciplinary collaboration and inspires innovative solutions for epidemic control.

Dr. Liang Zhang
Guest Editor

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Keywords

  • epidemic modelling
  • infectious disease dynamics
  • optimal control

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Published Papers (1 paper)

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Research

20 pages, 1149 KB  
Article
An Integrated Optimal Control Model for Simultaneous Tuberculosis Transmission and Stunting Prevention
by Rika Amelia, Nursanti Anggriani and Wan Muhamad Amir W. Ahmad
Mathematics 2026, 14(7), 1140; https://doi.org/10.3390/math14071140 - 28 Mar 2026
Viewed by 371
Abstract
This study develops an integrated mathematical model to investigate the interaction between tuberculosis (TB) transmission and childhood stunting, which is aligned with the United Nations Sustainable Development Goals (SDG 3). The population is structured into two age groups (0–5 years and ≥5 years), [...] Read more.
This study develops an integrated mathematical model to investigate the interaction between tuberculosis (TB) transmission and childhood stunting, which is aligned with the United Nations Sustainable Development Goals (SDG 3). The population is structured into two age groups (0–5 years and ≥5 years), with stunting explicitly incorporated into the pediatric population to capture its potential influence on TB dynamics. The model is formulated as a system of ordinary differential equations and analyzed using equilibrium and stability analysis, with the basic reproduction number, R0. The disease-free equilibrium is locally asymptotically stable when R0<1, while an endemic equilibrium exists when R0>1. Sensitivity analysis indicates that the transmission rate (β), progression rate from latent to active infection (σ), and recovery rate (γ) are the most influential parameters affecting R0. These parameters are therefore selected as control variables in an optimal control framework to design effective intervention strategies. Numerical simulations show that the combined control strategy significantly reduces TB transmission, resulting in a reduction of more than 80% in active TB cases within a relatively short intervention period. The results suggest that integrated interventions targeting transmission, disease progression, and recovery are substantially more effective than single-measure strategies. This study provides a quantitative framework to support integrated public health policies addressing TB and childhood stunting simultaneously. Full article
(This article belongs to the Special Issue Mathematical Modelling of Epidemic Dynamics and Control)
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