A scalable discrete event stochastic agent-based model of infectious disease propagation

We propose a newstochastic model of infectious disease propagation. This model tracks individual outcomes, but does so without needing to create connectivity graphs for all members of the population. This makes the model scalable to much larger populations than traditional agent-based models have been able to cope with, while preserving the impact of variability during the critical early stages of an outbreak. This contrasts favorably with aggregate deterministic models, which ignore variability, and negates the requirement to assume “convenient” but potentially unrealistic distribution choices which aggregate stochastic models need in order to be analytically tractable. Initial explorations with our new model show behaviors similar to the observed course of Ebola outbreaks over the past 30+ years-while many outbreaks will fizzle out relatively quickly, some appear to reach a critical mass threshold and can turn into widespread epidemics.