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. 2021 Mar:34:100426.
doi: 10.1016/j.epidem.2020.100426. Epub 2020 Dec 14.

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis

Alyson L Singleton  1 , Brandon D L Marshall  2 , S Bessey  2 , Matthew T Harrison  3 , Alison P Galvani  4 , Jesse L Yedinak  2 , Brendan P Jacka  2 , Steven M Goodreau  5 , William C Goedel  6
Affiliations

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis

Alyson L Singleton et al. Epidemics. 2021 Mar.

Abstract

As HIV incidence among people who inject drugs grows in the context of an escalating drug overdose epidemic in North America, investigating how network structure may affect vulnerability to rapid HIV transmission is necessary for preventing outbreaks. We compared the characteristics of the observed contact tracing network from the 2015 outbreak in rural Indiana with 1000 networks generated by an agent-based network model with approximately the same number of individuals (n = 420) and ties between them (n = 913). We introduced an initial HIV infection into the simulated networks and compared the subsequent epidemic behavior (e.g., cumulative HIV infections over 5 years). The model was able to produce networks with largely comparable characteristics and total numbers of incident HIV infections. Although the model was unable to produce networks with comparable cohesiveness (where the observed network had a transitivity value 35.7 standard deviations from the mean of the simulated networks), the structural variability of the simulated networks allowed for investigation into their potential facilitation of HIV transmission. These findings emphasize the need for continued development of injection network simulation studies in tandem with empirical data collection to further investigate how network characteristics played a role in this and future outbreaks.

Keywords: HIV; Injection drug use; Outbreak; Rural health; Social network analysis.

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Conflict of interest statement

Declaration of Competing Interest

None.

Figures

Fig. 1.
Fig. 1.. Distribution of simulated and observed network measures.
Boxplots displaying the distribution of values for eight network metrics for the set of simulated networks (n = 1000) compared to the observed Scott County network (shown with a blue circle). All values have been standardized around a mean value of 0 for visualization purposes. Transitivity has been excluded from this plot because the observed network represented an extreme outlying value compared to the simulated distributions (SD = 35.7).
Fig. 2.
Fig. 2.. Comparison of observed and realized degree distributions.
Line plots of the realized degree distributions of our simulated set of networks (grey curves) compared to the degree distribution of the observed network (blue curve).
Fig. 3.
Fig. 3.. Association of network metrics on the cumulative number of incident HIV infections.
Association of network metrics with the cumulative number of incident HIV infections in the simulated networks (n = 1000) compared to the observed data (shown with a blue circle).
Fig. 4.
Fig. 4.. Association of characteristics of the initial infection with the cumulative number of incident HIV infections.
Association of characteristics of the initial infection with the cumulative number of incident HIV infections in the simulated networks (n = 1000).
See this image and copyright information in PMC

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