Accounting for individual behaviors in a pandemic disease spread model

Mathematical models to predict the spread of disease during a pandemic largely require overly simplistic assumptions about disease transmission within populations. One significant shortcoming of these models is the inability to account for varying types and amount of contact between individuals, to address individuals´ behaviors or to assess the effectiveness of mitigation strategies. We present a non-homogeneous agent-based simulation of a pandemic in an urban population that accounts for individual behavior and transmission rates in different scenarios. The model is compact and parallelizable, and runs in reasonable computational time for an urban population of nearly five million individuals. Results are presented from modeling the spread of pandemic influenza in the Greater Toronto Area, Ontario, Canada.