  We propose a stochastic individual-based model of the progression of atrial
fibrillation (AF). The model operates at patient level over a lifetime and is
based on elements of the physiology and biophysics of AF, making contact with
existing mechanistic models. The outputs of the model are times when the
patient is in normal rhythm and AF, and we carry out a population-level
analysis of the statistics of disease progression. While the model is stylised
at present and not directly predictive, future improvements are proposed to
tighten the gap between existing mechanistic models of AF, and epidemiological
data, with a view towards model-based personalised medicine.
