ORIGINAL RESEARCH article
Front. Immunol.
Sec. Systems Immunology
This article is part of the Research TopicIntegrative Mathematical Models for Disease: Volume IIView all 4 articles
Heterogeneous immune recovery after viral response through a dynamical model of feedback-driven persistence and clearance
Provisionally accepted
- Beijing University of Technology, Beijing, China
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Viral infections trigger complex immune responses with heterogeneous outcomes shaped by nonlinear feedback. A system of ordinary differential equations is developed to investigate immune response dynamics during viral infection, incorporating six modules, including viral load, innate immunity, cellular immunity, humoral immunity, immune suppression, and IL-6. Bifurcation analysis reveals that under continuous viral exposure, when viral clearance rate and intrinsic viral death rate satisfy specific conditions, the system exhibits up to five stable equilibria. This indicates that different health and disease states may coexist depending on initial conditions, while severe inflammation mainly arises from strong activation of cellular immunity, highlighting the complexity of immune responses. Simulations of finite‑time viral exposure demonstrate multi‑timescale recovery characteristics, with viral load and IL‑6 levels declining rapidly, while humoral immune activation and immunosuppression show delayed and sustained patterns. Furthermore, analysis of infectious period and disease duration also indicates that during transition from early acute response to chronic disease, viral replication rate plays a critical role, while immune response intensity is sensitive to both viral clearance and immune self-activation. Subsystem analysis identifies the three-component subsystem of viral load, innate immunity, and cellular immunity as core drivers of bistability and oscillations, while humoral immunity, immune suppression, and IL-6 primarily modulate response amplitude and timing. This work establishes a theoretical framework for analyzing immune response and chronic risks through feedback dynamical modelling, providing insights for intervention strategies.
Keywords: bifurcation, mathematical modeling, multistability, subsystem analysis, Virus–immune dynamics
Received: 05 Oct 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Wang, Kang, Zhang and Zhuge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Changjing Zhuge
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