 |
Center for Nonlinear Studies |
At the core of all epidemic modeling approaches is the structure of human interactions, mobility and contacts patterns that finds its best representation in the form of networks. While for a long time detailed data on those networks were simply unavailable, the recent big data revolution is finally enabling the data-driven study of the interplay between epidemic processes and networks. Mathematical and computational methods have expanded into schemes that explicitly include spatial structures, individuals' heterogeneity and the multiple networks at play during the dynamic of an epidemic. These models have gained importance in the public-health domain, especially in infectious disease epidemiology, by providing quantitative analysis in support of the policy-making processes. In this seminar I will focus on discussing the recent successes as well as the methodological challenges in the modeling and forecast of network-driven contagion processes. Namely I will discuss the phenomenology emerging from the integration of multi-scale networks, the accuracy provided by different levels of data-integration, the problem of real-time estimation of parameters, and the validation through high quality data sets of the computational models.
Host: Aric Hagberg