Modeling and Detecting Downward Causation in Disease Transmission

We compare two epidemic models: the deterministic SIR (Susceptible-Infected-Recovered) model and the maximum-entropy-based DyMES (Dynamic Maxent across Entwined Scales) framework. In contrast to the widely used SIR model, the recently proposed DyMES model incorporates both top-down and bottom-up mechanisms and thus can capture local responses to global behavior. We simulate both models in three disease scenarios using parameter values drawn from the literature and analyze trends in the epidemic trajectories. Increasing the strength of downward causation lowers the peak but extends the time taken to return to the initial state, changing the trajectory from an SIR-like rise and fall to an epidemic with a long tail. By combining the Maxent inference procedure and top-down causation, the DyMES model predicts the probability distribution of disease incidence across population sub-aggregates.