SCS Colloquium: Combining social and genetic data to reconstruct HIV transmission networks

Inferring disease transmission networks is important in epidemiology
in order to understand and prevent the spread of infectious diseases.
Reconstruction of the infection transmission networks requires insight
into viral genome data as well as social interactions. For HIV-1
epidemic, the current research either uses genetic information of
patients virus to infer the past infection events or uses statistics
of sexual interactions to model the network structure of viral
spreading. Methods for a reliable reconstruction of HIV-1 transmission
dynamics, taking into account both molecular and societal data are
still lacking. The aim of this study was to combine information from
both genetic and epidemiological scales to characterize and analyse a
transmission network of the HIV-1 epidemic in central Italy. We
introduce a novel filter-reduction method to build a social network of
HIV infected patients. The social network is combined with a genetic
network of patients, to reconstruct the infection transmission
network. We apply this method to a cohort study of HIV-1 infected
patients in central Italy and find that patients who are highly
connected in the network have longer untreated infection periods. We
also find that the structure of the transmission network for
homosexual males is heterogeneous, consisting of a majority of
‘peripheral nodes’ that have only a few sexual interactions and a
minority of ‘hub nodes’ that have many sexual interactions. Inferring
HIV-1 transmission networks using combined epidemiological and genetic
networks reveals novel correlations between high out-degree
individuals and having longer untreated infection periods. These
findings signify the importance of early treatment and support the
potential benefit of wide population screening, management of early
diagnoses and anticipated antiretroviral treatment to prevent viral
transmission and spread. The approach presented here for
reconstructing HIV-1 transmission networks can have important
repercussions in the design of intervention strategies for disease
control.