A review of network simulation models of hepatitis C virus and HIV among people who inject drugs.
HIV
Hepatitis C
Network modelling
People who inject drugs
Review
Journal
The International journal on drug policy
ISSN: 1873-4758
Titre abrégé: Int J Drug Policy
Pays: Netherlands
ID NLM: 9014759
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
28
06
2019
revised:
17
09
2019
accepted:
04
10
2019
pubmed:
20
11
2019
medline:
6
8
2021
entrez:
20
11
2019
Statut:
ppublish
Résumé
Network modelling is a valuable tool for simulating hepatitis C virus (HCV) and HIV transmission among people who inject drugs (PWID) and assessing the potential impact of treatment and harm-reduction interventions. In this paper, we review literature on network simulation models, highlighting key structural considerations and questions that network models are well suited to address. We describe five approaches (Erdös-Rényi, Stochastic Block, Watts-Strogatz, Barabási-Albert, and Exponential Random Graph Model) used to model partnership formation with emphasis on the strengths of each approach in simulating different features of real-world PWID networks. We also review two important structural considerations when designing or interpreting results from a network simulation study: (1) dynamic vs. static network and (2) injection only vs. both injection and sexual networks. Dynamic network simulations allow partnerships to evolve and disintegrate over time, capturing corresponding shifts in individual and population-level risk behaviour; however, their high level of complexity and reliance on difficult-to-observe data has driven others to develop static network models. Incorporating both sexual and injection partnerships increases model complexity and data demands, but more accurately represents HIV transmission between PWID and their sexual partners who may not also use drugs. Network models add the greatest value when used to investigate how leveraging network structure can maximize the effectiveness of health interventions and optimize investments. For example, network models have shown that features of a given network and epidemic influence whether the greatest community benefit would be achieved by allocating hepatitis C or HIV treatment randomly, versus to those with the most partners. They have also demonstrated the potential for syringe services and "buddy sharing" programs to reduce disease transmission.
Identifiants
pubmed: 31740175
pii: S0955-3959(19)30280-4
doi: 10.1016/j.drugpo.2019.10.006
pmc: PMC8729792
mid: NIHMS1761091
pii:
doi:
Substances chimiques
Pharmaceutical Preparations
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
102580Subventions
Organisme : NCHHSTP CDC HHS
ID : U38 PS004644
Pays : United States
Organisme : NIDA NIH HHS
ID : P30 DA040500
Pays : United States
Organisme : NIAID NIH HHS
ID : P30 AI042853
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA015612
Pays : United States
Organisme : NIDA NIH HHS
ID : R37 DA015612
Pays : United States
Organisme : CDC HHS
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA046527
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Interests The authors declare no conflicts of interest.
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