A review of network simulation models of hepatitis C virus and HIV among people who inject drugs

Bellerose, Meghan; Zhu, Lin; Hagan, Liesl M.; Thompson, W.; Randall, Laura; Malyuta, Yelena; Salomon, Joshua A.; Linas, Benjamin P.

doi:10.1016/j.drugpo.2019.10.006

Cited by 10 publications

(11 citation statements)

References 83 publications

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“…To further assist with the development of a realistic synthetic population, we also fitted an exponential random graph model (ERGM) using the network estimates from the meta-analysis. ERGMs offer a uniquely robust and flexible approach in modeling the dependencies between behavioral parameters, and have been used to model syringe sharing networks in a number of related contexts [30].…”

Section: Introductionmentioning

confidence: 99%

People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination

et al. 2022

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Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets set by the World Health Organization, as the opioid crisis continues to drive both injection drug use and increasing HCV incidence. A pragmatic approach to achieving this is using a microelimination approach of focusing on high-risk populations such as people who inject drugs (PWID). Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and treatment uptake to achieve HCV microelimination. However, these models need to be informed with realistic sociodemographic, risk behavior and network estimates on PWID. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with PWID in metropolitan Chicago to produce parameters for a synthetic population for realistic computational models (e.g., agent-based models). We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the network component of the synthetic population.

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Section: Introductionmentioning

confidence: 99%

People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination

et al. 2022

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“…Micro-environment refers to the composition and behavior of people most proximal to PWID, who may co-engage in risk behaviors (e.g., sexual and substance use) and may also provide important sources of social support and stabilization ( Bellerose et al, 2021 ; De et al, 2007 , 2008 ). Our framework includes four distinct but overlapping micro-environment networks: substance-using, sexual, work-related, and social networks.…”

Section: Resultsmentioning

confidence: 99%

A stakeholder-driven framework for measuring potential change in the health risks of people who inject drugs (PWID) during the COVID-19 pandemic

Bradley¹,

Austin²,

Allen³

et al. 2022

International Journal of Drug Policy

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“…To match this property, we employed the Watts-Strogatz model to generate complex networks as the initial sociocentric social networks of our agent-based model. The Watts-Strogatz model is a classic generation method for small-world networks (Watts & Strogatz, 1998 ) and has been widely applied in social network studies (Bellerose et al, 2021 ; Nowak et al, 2016 ; Rolfe, 2014 ). It requires three parameters: the network size, average degree, and rewiring probability.…”

Section: Methodsmentioning

confidence: 99%

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach

Wang

Lin

2022

Curr Psychol

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Despite converging evidence for the importance of relational mobility on shaping people’s social experiences, previous work suggested mixed findings for its influence on the structure of sociocentric networks, which lays the basis for the development of all types of social relationships. Additionally, as it is timely and economically intractable to administer such longitudinal experiments in real-life settings, most previous work mainly relied on cross-sectional correlation analyses and provided limited causal evidence. The current research used an agent-based modeling approach to examine whether higher relational mobility (i.e., the number of opportunities to meet new people) would promote integration among social networks over time. Using parameters derived from survey data, we simulated how the integration of sociocentric social networks evolves under different levels of relational mobility. Based on the data of three network structural indicators, including modularity, global efficiency, and standard deviation of nodal betweenness, we obtained causal evidence supporting that higher relational mobility promotes greater network integration. These findings highlight the power of socioecological demands on our social experiences. Supplementary Information The online version contains supplementary material available at 10.1007/s12144-022-03130-x.

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A review of network simulation models of hepatitis C virus and HIV among people who inject drugs

Cited by 10 publications

References 83 publications

People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination

People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination

A stakeholder-driven framework for measuring potential change in the health risks of people who inject drugs (PWID) during the COVID-19 pandemic

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach

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