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Hypothetical illustration of the mapping process. This example involves two behavioral clusters (panel (A); light blue and black) and three transmission clusters (TC1, TC2 and TC3 in panel (B)). The mapping shown in panel (C) suggests assortativity among members of the light blue behavioral cluster in transmission cluster TC1 (containing only 2 members of the black behavioral cluster). Analogously, transmission cluster TC2 is exclusively formed by members of the black behavioral cluster (an unlikely observation if mixing was at random). Members of both behavioral clusters distribute evenly along transmission cluster TC3.
Source publication
We hypothesize that patterns of sexual behavior play a role in the conformation of transmission networks, i.e., the way you behave might influence whom you have sex with. If that was the case, behavioral grouping might in turn correlate with, and potentially predict transmission networking, e.g., proximity in a viral phylogeny. We rigorously presen...
Citations
... In terms of public health practice, molecular epidemiology has been used routinely in North America and Europe to identify local patterns of active transmission, which have supported focused HIV prevention programmes and interventions (Bachmann et al., 2021;Brenner et al., 2021;Nguyen et al., 2022;Oster et al., 2021;Poon et al., 2015;Poon et al., 2016;Ragonnet-Cronin et al., 2018;Ragonnet-Cronin et al., 2022;Salazar-Vizcaya et al., 2022;Schneider et al., 2022;Tumpney et al., 2020;Wilbourn et al., 2021). Recently, HIV molecular epidemiology in Eastern and Southern Africa has been thrust into the spotlight. ...
Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in ‘real world’ programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team’s protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process – The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.
... An exploratory analysis of the association of SHCS center and STI was suggestive of this. This hypothesis can be more adequately tested using phylogenetic data, as was explored in a recent proof of concept study using HCV phylogenies [23]. ...
Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors.
