Speaker
Description
There are substantial differences between patterns of genetic similarity clusters seen in HIV epidemics of the Global North and Southern Africa, prompting questions about factors contributing to these differences. Unlike patterns observed in the Global North, Southern African epidemics contain lower clustering rates and a scarcity of large clusters.
Here we describe an exploration of potential causes of these differences, encompassing potential technical and epidemiological causes. Technical causes include variations in sampling fractions and biased sampling of primary transmitting subpopulations, as well as differences in sampling delays. Epidemiological causes include differences in the biology or virology of transmission between men who have sex with men (MSM) and heterosexual (HET) populations (with a higher per-contact transmission rate in MSM), varying population sizes of MSM and HET epidemics, and differences in transmission networks (such as the presence of few super-spreaders, few rapid transmission chains, and transmission driven by large groups at moderate risk rather than core populations with skewed offspring distributions). Some evidence exists to support a strong effect of the transmission network.
We attempt to quantify the impact of each of these factors on the observed cluster pattern differences by using a simple branching process model. Examining each factor reveals a multifaceted landscape where sampling coverage, sampling delays, transmission risk, sexual network dynamics, among potentially other factors, each can exert influences on clustering patterns. Leveraging relationships between parameters offers a strategic approach to reduce parametric uncertainty, contributing to a nuanced understanding of these dynamics.
By using modeling approaches and considering technical and epidemiological dimensions, our study provides essential insights for designing targeted HIV prevention strategies in diverse populations.
