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Optimal control of a multi-scale HIV-opioid model

  • ,
  • Necibe Tuncer
    ,
  • Maia Martcheva(corresponding author)
*Corresponding author for this work
  • ,
  • Florida Atlantic University
    ,
  • University of Florida
Scholary Output:
Contribution to journal
Article
Peer-review

Open access

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

Abstract

In this study, we apply optimal control theory to an immuno-epidemiological model of HIV and opioid epidemics. For the multi-scale model, we used four controls: treating the opioid use, reducing HIV risk behaviour among opioid users, entry inhibiting antiviral therapy, and antiviral therapy which blocks the viral production. Two population-level controls are combined with two within-host-level controls. We prove the existence and uniqueness of an optimal control quadruple. Comparing the two population-level controls, we find that reducing the HIV risk of opioid users has a stronger impact on the population who is both HIV-infected and opioid-dependent than treating the opioid disorder. The within-host-level antiviral treatment has an effect not only on the co-affected population but also on the HIV-only infected population. Our findings suggest that the most effective strategy for managing the HIV and opioid epidemics is combining all controls at both within-host and between-host scales.

Publication Information

Output type

Scholary Output:
Contribution to journal
Article
Peer-review

Original language

English (US)

Article number

2317245

Journal (Volume, Issue Number)

Journal of biological dynamics (Volume 18, Issue 1)

Publication milestones

  • Published - 2024

Publication status

Published - 2024

ISSN

1751-3758

Publication IDs

  • Scopus: 85185296115
  • PubMed: 38369811

Publication metrics

Metrics

Scopus
citations
Fractional count
1
Fractional count
0.33
Fractional count
2
Fractional count
0.67
Fractional count
1
Fractional count
1

Funding Details

Maia Martcheva acknowledges partial support from National Science Foundation (NSF) [grant number DMS-1951595]. Necibe Tuncer acknowledges partial support from National Science Foundation (NSF) [grant number DMS-195626].
FunderFunding numbers
NSF
DMS-1951595, DMS-195626