Phase transition in spatial epidemics using cellular automata with noise

One of the central issues in studying the complex population patterns observed in nature is the role of stochasticity. In this paper, the effects of additive spatiotemporal random variations-noise-are introduced to an epidemic model. The no-noise model exhibits a phase transition from a disease-free state to an endemic state. However, this phase transition can revert in a resonance-like manner depending on noise intensity when introducing nonzero random variations to the model. On the other hand, given a regime where disease can persist, noise can induce disappearance of the phase transition. The results obtained show that noise plays a tremendous role in the spread of the disease state, which has implications for how we try to prevent, and eventually eradicate, disease.