Faculty Bibliography

BACKGROUND:New diagnoses of HIV-1 infection among people who inject drugs (PWID) increased significantly during 2011 in Athens. OBJECTIVE:Our aim was to investigate the patterns of HIV epidemic spread among PWID and to estimate the transmission dynamics for the major local transmission networks (LTNs). METHODS:We analyzed sequences from 2,274 HIV-infected subjects sampled in Greece during 01/01/2011-31/10/2014. Of specimens in our sample, 874 sequences were isolated from HIV-infected PWID. Phylodynamic analysis was performed using birth-death serial skyline models. RESULTS:Phylogenetic analysis revealed that the majority of sequences from PWID (N=746, 85.4%) fell within four LTNs: CRF14_BG (N=456, 58.3%), CRF35_AD (N=149, 19.1%), subtype B (N=118, 15.1%) and A1 (N=59, 7.5%). In addition to PWID, we also found that sequences from 36 non-PWID belonged to the LTNs corresponding to cross-group transmissions. Based on the estimated plots of the effective reproductive number (Re) over time, subtype A1 and CRF35_AD LTNs showed a sharp increase before and during 2011 (maximum value of Re=3.0 and Re=4.6, respectively). For subtype B and CRF14_BG LTNs, the Re was increasing until the end of 2012 (maximum value of Re=3.2 and Re=3.0, respectively). CONCLUSION:HIV transmissions within subtype A1 and CRF35_AD LTNs increased sharply during the early stage of the outbreak, in contrast to subtype B and CRF14_BG. A significant reduction in the number of infections was estimated on all transmission networks from the beginning of 2013 onwards. Prevention measures that took place in the Athens metropolitan area at the end of 2012 including also the ARISTOTLE program may explain this decrease.

Early treatment, soon after infection, reduces HIV transmissions and benefits patients. The Transmission Reduction Intervention Project (TRIP) evaluated a network intervention to detect individuals recently infected (in the past 6 months). TRIP was conducted in Greece (2013-2015) and focused on drug injector networks. Based on HIV status, testing history, and the results of an assay to detect recent infections, TRIP classified drug injector "Seeds" into groups: Recent Seeds (RS), and Control Seeds with Long-term HIV infection (LCS). The network members of RS and LCS were traced for two steps. The analysis included 23 RS, 171 network members of the RS, 19 LCS, and 65 network members of the LCS. The per-seed number of recents detected in the network of RS was 5 times the number in the network of LCS (Ratio RS vs. LCS: 5.23; 95% Confidence Interval (CI): 1.54-27.61). The proportion of recents among HIV positives in the network of RS (27%) was approximately 3 times (Ratio RS vs. LCS: 3.30; 95% CI: 1.04-10.43) that in the network of LCS (8%). Strategic network tracing that starts with recently infected persons could support public health efforts to find and treat people early in their HIV infection.

The number of public health applications for molecular epidemiology and social network analysis has increased rapidly since the improvement in computational capacities and the development of new sequencing techniques. Currently, molecular epidemiology methods are used in a variety of settings: from infectious disease surveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic developments, and plan preventive interventions. Social network methods are used to understand how infections spread through communities and what the risk factors for this are, as well as in improved contact tracing and message-dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully sufficient information on infection transmission pathways. The main differences between the two data sources are that, firstly, social network data include uninfected individuals unlike the molecular data sampled only from infected network members. Thus, social network data include more detailed picture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have been attempts to combine and compare the data obtained from the two sources. Even though there is no consensus on whether and how social and genetic data complement each other, this research might significantly improve our understanding of how viruses spread through communities.