Vollständiger Abstract
Worum geht es in dieser Arbeit?
Hepatitis C virus (HCV) infection remains highly prevalent among people who inject drugs (PWID), where treatment effectiveness is strongly influenced by adherence and behavioral relapse. In this study, we develop and analyze a deterministic compartmental model to examine the impact of treatment adherence, relapse, and risk-reduction behaviors on HCV transmission dynamics. The model incorporates key behavioral pathways, including treatment initiation, partial adherence, and relapse to high-risk behavior. Analytical results establish positivity and boundedness of solutions, and the basic reproduction number is derived using the next-generation matrix approach. We show that the disease-free equilibrium is globally asymptotically stable when the reproduction number is below unity, while uniform persistence and a globally stable endemic equilibrium occur when transmission exceeds this threshold. Numerical simulations and sensitivity analyses indicate that the probability of transmission per needle-sharing event and the number of sharing partners are dominant drivers of epidemic persistence. In contrast, treatment uptake and sustained adherence significantly reduce transmission. Relapse to drug misuse undermines treatment benefits by expanding the infectious pool. These findings underscore the importance of integrating antiviral therapy with behavioral support to achieve long-term HCV control among PWID.
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Publikationsdaten
- Autor:innen
- Mlyashimbi Helikumi, Tinashe Victor Mupedza, Shingirai Tangakugara Murambiwa, Moster Zhangazha, Adquate Mhlanga
- Quelle
- Mathematical and Computational Applications
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2297-8747
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Zitierfähiger Nachweis
Mlyashimbi Helikumi, Tinashe Victor Mupedza, Shingirai Tangakugara Murambiwa, Moster Zhangazha, Adquate Mhlanga (2026). Modeling the Impact of Treatment Adherence on Hepatitis C Transmission Dynamics Among Intravenous Drug Users. Mathematical and Computational Applications. https://doi.org/10.3390/mca31050176
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