Modeling Degenerative Disc Disease under a Stochastic Disease Random Branching Process

Degenerative disc disease (DDD) is a leading source of neck and lower back pain, especially among older adults and individuals in high‐load occupations. Clinical practice currently struggles to predict DDD quantitatively, delaying effective intervention. This project proposes a three‐stage, continuous stochastic model describing disc degeneration initiation, progression, and propagation to adjacent discs. By incorporating measurable disc indicators — such as height loss, displacement, and annulus tears — into a physics–statistics‐based framework, we derive DDD metrics that estimate disc lifespans, the probability of multi‐level degeneration, and time‐to‐pain events. We then interpret these metrics to guide personalized decisions about whether to treat only severely degenerated discs or also discs likely to degenerate soon, factoring in age, occupation, and lifestyle. With real‐time MRI data, the model updates dynamically, strengthening its clinical relevance. Our findings could enhance early detection, inform optimal surgical timing, and improve outcomes for at‐risk populations, including Hong Kong’s aging workforce.