Modeling the spread of HIV and HCV infections based on identification and characterization of high-risk communities using social media

Abstract

Epidemiological dynamics of diseases, which may be transmitted due to sexual behavior or injecting drug use, can vary across demographic, socio-behavioral, and geographic population groups. Typically, studies modeling infection dissemination in such settings use simulated data and employ simplified contact networks. Here, we demonstrate feasibility of simulating HIV/HCV epidemics over a real-world contact network inferred using social media mining. Such networks can lead to more realistic modeling of disease transmission patterns in high-risk population than what is possible at the current state-of-the-art. In particular, we studied how topological characteristics of transmission networks are reflected by viral phylogenies.