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Mather, Dineli R; Crofts, Nick

doi:10.1023/a:1007548307196

Cited by 26 publications

(4 citation statements)

References 11 publications

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“…Early models of HCV epidemics among PWID utilized basic susceptible-infected (SI) formulations within a variety of modeling approaches (stochastic infividual-based and deterministic compartmental, HCV-monoinfection and HIV/HCV joint coinfection) to examine transmission dynamics in a range of theoretical and real-world settings [7–15]. These models simulated open populations, with injectors entering open initiation of injecting and exiting the PWID population through permanent cessation of injecting or death.…”

Section: Modeling Hcv Transmission and Elimination Among Pwidmentioning

confidence: 99%

Mathematical modeling of hepatitis c virus (HCV) prevention among people who inject drugs: A review of the literature and insights for elimination strategies

Pitcher¹,

Bórquez

Skaathun

et al. 2019

Journal of Theoretical Biology

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In 2016, the World Health Organization issued global elimination targets for hepatitis C virus (HCV), including an 80% reduction in HCV transmission by 2030. The vast majority of new HCV infections occur among people who inject drugs (PWID), and as such elimination strategies require particular focus on this population. As governments urgently require guidance on how to achieve elimination among PWID, mathematical modeling can provide critical information on the level and targeting of intervention are required. In this paper we review the epidemic modeling literature on HCV transmission and prevention among PWID, highlight main differences in mathematical formulation, and discuss key insights provided by these models in terms of achieving WHO elimination targets among PWID. Overall, the vast majority of modeling studies utilized a deterministic compartmental susceptible-infected-susceptible structure, with select studies utilizing individual-based network transmission models. In general, these studies found that harm reduction alone is unlikely to achieve elimination targets among PWID. However, modeling indicates elimination is achievable in a wide variety of epidemic settings with harm reduction scale-up combined with modest levels of HCV treatment for PWID. Unfortunately, current levels of testing and treatment are generally insufficient to achieve elimination in most settings, and require further scale-up. Additionally, network-based treatment strategies as well as prison-based treatment and harm reduction provision could provide important additional population benefits. Overall, epidemic modeling has and continues to play a critical role in informing HCV elimination strategies worldwide.

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Section: Modeling Hcv Transmission and Elimination Among Pwidmentioning

confidence: 99%

Mathematical modeling of hepatitis c virus (HCV) prevention among people who inject drugs: A review of the literature and insights for elimination strategies

Pitcher¹,

Bórquez

Skaathun

et al. 2019

Journal of Theoretical Biology

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show abstract

“…[15]–[22], and [2], [14], [23], [24] in the Australian context) or have lacked an empirically grounded contact network [25]. Under a homogeneous mixing assumption, members of a population are assumed to have contact with all other members of the population [26].…”

Section: Introductionmentioning

confidence: 99%

Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs

et al. 2013

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Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from “less-” to “more-frequent” injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.

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“…It was interesting to see that many of the existing models focus on elimination efforts in Europe, the U.S., and Australia, while only two models targeted low-and middle-income countries, such as Vietnam and Pakistan [16,17]. Various modeling approaches were developed to examine transmission dynamics of HCV, including stochastic individual-based and deterministic compartmentalization models [18][19][20][21][22][23][24]. These models utilized basic susceptible-infected (SI) formulations among people who inject drugs.…”

Section: Mathematical Modeling and Infectious Diseasesmentioning

confidence: 99%

Non-Invasive Diagnosis of Liver Fibrosis in Chronic Hepatitis C using Mathematical Modeling and Simulation

2022

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Hepatitis C is a viral infection (HCV) that causes liver inflammation, and it was found that it affects over 170 million people around the world, with Egypt having the highest rate in the world. Unfortunately, serial liver biopsies, which can be invasive, expensive, risky, and inconvenient to patients, are typically used for the diagnosis of liver fibrosis progression. This study presents the development, validation, and evaluation of a prediction mathematical model for non-invasive diagnosis of liver fibrosis in chronic HCV. The proposed model in this article uses a set of nonlinear ordinary differential equations as its core and divides the population into six groups: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. The validation approach involved the implementation of two equivalent simulation models that examine the proposed process from different perspectives. A system dynamics model was developed to understand the nonlinear behavior of the diagnosis process over time. The system dynamics model was then transformed to an equivalent agent-based model to examine the system at the individual level. The numerical analysis and simulation results indicate that the earlier the HCV treatment is implemented, the larger the group of people who will become responders, and less people will develop complications such as fibrosis.

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Untitled

Cited by 26 publications

References 11 publications

Mathematical modeling of hepatitis c virus (HCV) prevention among people who inject drugs: A review of the literature and insights for elimination strategies

Mathematical modeling of hepatitis c virus (HCV) prevention among people who inject drugs: A review of the literature and insights for elimination strategies

Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs

Non-Invasive Diagnosis of Liver Fibrosis in Chronic Hepatitis C using Mathematical Modeling and Simulation

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