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Dynamic modeling of hepatitis C transmission among people who inject drugs
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 42020 (English)In: Epidemics, ISSN 1755-4365, E-ISSN 1878-0067, Vol. 30, article id 100378Article in journal (Refereed) Published
Abstract [en]

To reach the WHO goal of hepatitis C elimination, it is essential to identify the number of people unaware of their hepatitis C virus (HCV) infection and to investigate the effect of interventions on the disease transmission dynamics. In many high-income countries, one of the primary routes of HCV transmission is via contaminated needles shared by people who inject drugs (PWIDs). However, substantial underreporting combined with high uncertainty regarding the size of this difficult to reach population, makes it challenging to estimate the core indicators recommended by the WHO. To support progress toward the elimination goal, we present a novel multi-layered dynamic transmission model for HCV transmission within a PWID population. The model explicitly accounts for disease stage (acute and chronic), injection drug use status (active and former PWIDs), status of diagnosis (diagnosed and undiagnosed) and country of disease acquisition (domestic or abroad). First, based on this model, and using routine surveillance data, we estimate the number of undiagnosed PWIDs, the true incidence, the average time until diagnosis, the reproduction numbers and associated uncertainties. Second, we examine the impact of two interventions on disease dynamics: (1) direct-acting antiviral drug treatment, and (2) needle exchange programs. As a proof of concept, we illustrate our results for a specific data set. In addition, we develop a web application to allow our model to be explored interactively and with different parameter values.

Place, publisher, year, edition, pages
2020. Vol. 30, article id 100378
Keywords [en]
Dynamic modeling, Hepatitis C, Treatment, Needle exchange programs, Surveillance data, Inference, Shiny app
National Category
Infectious Medicine Microbiology in the medical area Mathematics
Identifiers
URN: urn:nbn:se:su:diva-181052DOI: 10.1016/j.epidem.2019.100378ISI: 000522664200003PubMedID: 31864130OAI: oai:DiVA.org:su-181052DiVA, id: diva2:1429266
Available from: 2020-05-09 Created: 2020-05-09 Last updated: 2022-03-23Bibliographically approved

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Kuhlmann-Berenzon, SharonBritton, Tom

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