A Multiscale Dynamical-Systems Model of Measles Immuno-Epidemiology with ODE-to-Cellular-Automaton Coupling

Measles virus infection couples nonlinear processes across biological scales, including within-host viral amplification, immune-cell depletion, delayed adaptive control, persistent viral RNA, heterogeneous host severity and vaccination-dependent population spread. A multiscale mathematical framework is developed by coupling a seven-variable within-host ordinary differential equation model to a stochastic cellular automaton. The within-host system extends a four-variable measles immunodynamics core by including IFN-γ-dominant and IL-17-associated immune responses, persistent viral RNA and neutralizing antibodies. Six host archetypes are represented as structured parameter perturbations of this common dynamical core. The principal novelty is an explicit cross-scale coupling operator that separates genuinely ODE-derived host descriptors from hybrid epidemiological mapping rules and independently specified population-level contact and susceptibility assumptions, allowing within-host heterogeneity to propagate transparently into a spatial stochastic epidemic model. An explicit ODE-to-cellular-automaton map translates within-host trajectories into infectious timing, daily infectivity profiles and an illustrative ODE-informed severity-to-death transition mapping used internally by the cellular automaton. The mortality map depends on viral burden, infectious duration, IFN-γ deficit, cumulative infectivity and an immune-deficit–infectivity interaction term. Population simulations show a nonlinear reduction in attack rate with increasing vaccination coverage, reduced modeled death burden under targeted high-risk in silico perturbations and additional suppression under reactive vaccination campaigns. A direct local cellular-automaton secondary-infection estimate is reported instead of interpreting cumulative infectivity burden as a reproduction number. A targeted contact-structure sensitivity further shows that matching the expected local direct-secondary-infection potential does not imply equivalent population-level attack rates, emphasizing that the quantitative CA outcomes are geometry specific. Sobol sensitivity analysis with convergence up to Nbase=4096 identifies core viral and immune parameters as dominant drivers of within-host and multiscale outputs. The framework provides an explicit dynamical-systems approach for coupling differential-equation immunodynamics to spatial stochastic population models in mathematical biology.