Dynamics and Control of Infections on Social Networks

TL;DR

This paper introduces a network-based method to analyze HIV transmission dynamics among high-risk groups, revealing key transmission pathways and informing targeted intervention strategies.

Contribution

It develops a novel technique using routine sampling data to trace infection flow between population groups in HIV epidemics, enhancing understanding of transmission and control measures.

Findings

HIV transmission was highest from female sex workers to male clients

Most infections occurred among female partners of male clients

Controlling transmission among injection drug users effectively reduces overall infections

Abstract

Random mixing in host populations has been a convenient simplifying assumption in the study of epidemics, but neglects important differences in contact rates within and between population groups. For HIV/AIDS, the assumption of random mixing is inappropriate for epidemics that are concentrated in groups of people at high risk, including female sex workers (FSW) and their male clients (MCF), injection drug users (IDU) and men who have sex with men (MSM). To find out who transmits infection to whom and how that affects the spread and containment of infection remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM). Calculation of the case reproduction numbers within and between groups, and for the whole network, provides insights into control that cannot be deduced simply from observations on the prevalence of infection. Specifically, the per capita rate of HIV transmission was highest from FSW to MCF, and most HIV infections occurred in FPM, but the number of infections in the whole network is best reduced by interrupting transmission to and from IDU. This network analysis can be used to guide HIV/AIDS interventions based on needle exchange, condom distribution and antiretroviral therapy. The method requires only routine data and could be applied to infections in other populations.