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.
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Jha, D., Skums, P., Zelikovsky, A., Khudyakov, Y., Singh, R. (2017). Modeling the Spread of HIV and HCV Infections Based on Identification and Characterization of High-Risk Communities Using Social Media. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_46
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DOI: https://doi.org/10.1007/978-3-319-59575-7_46
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