The role of the airline transportation network in the prediction and predictability of global epidemics

TLDR

Large-scale networks exhibit heterogeneity and unbounded fluctuations that profoundly shape diffusion dynamics. The study introduces a stochastic computational framework that uses the global airline network and population data to forecast epidemics and assess how network heterogeneity influences disease spread. The authors define quantitative metrics that capture the heterogeneity and predictability of epidemic spread across the airline network. The resulting metrics enable analysis of containment strategies and assessment of epidemic risk.

Abstract

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.