SCS Colloquium on Monday, 24-01-2011, Room D1.112

In this regard, social and computational scientists have been trying to study spread of disease in a population using social and sexual networks. They model the population as a complex network and run models of disease on top of that. In the case of HIV, they have used these models to understand the transmission of HIV and drug resistance in the population. On the other hand, phylogenetists have been using bottom-up approaches to infer the transmission and evolution of HIV. They build phylogenetic trees using genetic information of virused and mathematical methods. HIV Phylogenetic tree shows the evolutionary relation between HIV sequences in a population which can be used to infer the transmission of HIV in the population. However, these methods have limitations and may not necessarily accurately represent the evolution and transmission due to having noise in the data and sensitivity of the methods.

We are trying to use information in both scales (genetic and social) to infer the transmission routs of HIV in a population. The multi-scale approach helps us to overcome the limitations and obtain a better understanding of the network underlying HIV transmission in a population. So far, we have built a network of HIV sequences based on their social information using a reductionism approach and analyzed the basic characteristics of this network (degree distribution,..). Sub-networks corresponding to different HIV risk groups (MSM, Heterosexual, IDU,..) are also analyzed separately. We defined a social distance between every two individuals in the network and compared them with the genetic distances that are measured through the phylogenetic analysis. Furthermore we are trying to overlay this social network of HIV sequences with the genetic one using genetic, epidemiologic and social network analysis, and visualization techniques.