Digital twin frameworks for polio and post-polio neurodegeneration: Toward predictive, personalised lifelong neuro-rehabilitation

Poliomyelitis is a neuroinvasive viral disease that, despite near-global eradication, has left millions of survivors worldwide with lifelong motor-system injury. Many develop delayed neurological decline decades later in the form of Post-polio syndrome, characterised by progressive weakness, fatigue, pain, and respiratory compromise. Disease trajectories are highly heterogeneous, unpredictable, and shaped by complex interactions between residual motor-neuron pools, muscle adaptation, ageing, metabolism, and immune tone. Conventional care relies on episodic clinical review and reactive rehabilitation, with limited capacity for long-term personalised forecasting. Digital-twin technology, defined as an adaptive computational model that continuously mirrors an individual's biological and functional state, offers a transformative framework for predictive and precision-guided lifelong polio care. By integrating neuromuscular physiology, biomechanics, metabolic status, wearable sensor data, and rehabilitation responses, digital twins could enable real-time modelling of motor-unit decline, functional reserve, respiratory vulnerability, and fatigue dynamics. This article outlines a focused conceptual framework for polio digital twins, emphasising model architecture, feedback loops, and translational applications, rather than virological biology. The proposed framework positions digital twins as a missing precision-medicine layer for a neglected global survivor population.