Abstract for: Comparison between Individual-based and Aggregate Models in the Context of Tuberculosis Transmission

The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper com-pares the difference between aggregate models and individual-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor. The merits and impact of capturing individual heterogeneity is examined via representing Bacillus Calmette-Gurin vaccination and reactivation in both models. The simulation results of the two models exhibit distinct discrepancies in TB incidence rate and prevalence. Results also suggest that, at the level of practical application, individual-based models offer significantly greater accuracy and easier extension, especially when representing a decreasing reactivation rate, waning of immunity and heterogeneous individual at- tributes. Another experiment sought to evaluate the impact of network structure on TB diffusion. Simulations are conducted under three widely used network topologies, namely random, scale-free and small world. The results reveal large differences between results of individual-based models and aggregate models, which further give insights into the difference between these two model types in the context of practical decision-making.