Research article Special Issues

A network immuno-epidemiological model of HIV and opioid epidemics


  • Received: 20 September 2022 Revised: 08 November 2022 Accepted: 18 November 2022 Published: 19 December 2022
  • In this paper, we introduce a novel multi-scale network model of two epidemics: HIV infection and opioid addiction. The HIV infection dynamics is modeled on a complex network. We determine the basic reproduction number of HIV infection, $ \mathcal{R}_{v} $, and the basic reproduction number of opioid addiction, $ \mathcal{R}_{u} $. We show that the model has a unique disease-free equilibrium which is locally asymptotically stable when both $ \mathcal{R}_{u} $ and $ \mathcal{R}_{v} $ are less than one. If $ \mathcal{R}_{u} > 1 $ or $ \mathcal{R}_{v} > 1 $, then the disease-free equilibrium is unstable and there exists a unique semi-trivial equilibrium corresponding to each disease. The unique opioid only equilibrium exist when the basic reproduction number of opioid addiction is greater than one and it is locally asymptotically stable when the invasion number of HIV infection, $ \mathcal{R}^{1}_{v_i} $ is less than one. Similarly, the unique HIV only equilibrium exist when the basic reproduction number of HIV is greater than one and it is locally asymptotically stable when the invasion number of opioid addiction, $ \mathcal{R}^{2}_{u_i} $ is less than one. Existence and stability of co-existence equilibria remains an open problem. We performed numerical simulations to better understand the impact of three epidemiologically important parameters that are at the intersection of two epidemics: $ q_v $ the likelihood of an opioid user being infected with HIV, $ q_u $ the likelihood of an HIV-infected individual becoming addicted to opioids, and $ \delta $ recovery from opioid addiction. Simulations suggest that as the recovery from opioid use increases, the prevalence of co-affected individuals, those who are addicted to opioids and are infected with HIV, increase significantly. We demonstrate that the dependence of the co-affected population on $ q_u $ and $ q_v $ are not monotone.

    Citation: Churni Gupta, Necibe Tuncer, Maia Martcheva. A network immuno-epidemiological model of HIV and opioid epidemics[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 4040-4068. doi: 10.3934/mbe.2023189

    Related Papers:

  • In this paper, we introduce a novel multi-scale network model of two epidemics: HIV infection and opioid addiction. The HIV infection dynamics is modeled on a complex network. We determine the basic reproduction number of HIV infection, $ \mathcal{R}_{v} $, and the basic reproduction number of opioid addiction, $ \mathcal{R}_{u} $. We show that the model has a unique disease-free equilibrium which is locally asymptotically stable when both $ \mathcal{R}_{u} $ and $ \mathcal{R}_{v} $ are less than one. If $ \mathcal{R}_{u} > 1 $ or $ \mathcal{R}_{v} > 1 $, then the disease-free equilibrium is unstable and there exists a unique semi-trivial equilibrium corresponding to each disease. The unique opioid only equilibrium exist when the basic reproduction number of opioid addiction is greater than one and it is locally asymptotically stable when the invasion number of HIV infection, $ \mathcal{R}^{1}_{v_i} $ is less than one. Similarly, the unique HIV only equilibrium exist when the basic reproduction number of HIV is greater than one and it is locally asymptotically stable when the invasion number of opioid addiction, $ \mathcal{R}^{2}_{u_i} $ is less than one. Existence and stability of co-existence equilibria remains an open problem. We performed numerical simulations to better understand the impact of three epidemiologically important parameters that are at the intersection of two epidemics: $ q_v $ the likelihood of an opioid user being infected with HIV, $ q_u $ the likelihood of an HIV-infected individual becoming addicted to opioids, and $ \delta $ recovery from opioid addiction. Simulations suggest that as the recovery from opioid use increases, the prevalence of co-affected individuals, those who are addicted to opioids and are infected with HIV, increase significantly. We demonstrate that the dependence of the co-affected population on $ q_u $ and $ q_v $ are not monotone.



    加载中


    [1] National Institute of Drug Abuse (NIH), Opioid overdose crisis, 2021. Available from: https://www.drugabuse.gov/drug-topics/opioids/opioid-overdose-crisis.
    [2] U.S. Department of Health and Human Services, What is the U.S. opioid epidemic? 2019. Available from: https://www.hhs.gov/opioids/about-the-epidemic/index.html.
    [3] National Institute for Drug Abuse (NIH), Drug use and viral infections (HIV, hepatitis), 2020. Available from: https://www.drugabuse.gov/publications/drugfacts/drug-use-viral-infections-hiv-hepatitis.
    [4] S. L. Hodder, J. Feinberg, S. A. Strathdee, S. Shoptaw, F. L. Altice, L. Ortenzio, et al., The opioid crisis and HIV in the USA: deadly synergies, Lancet, 397 (2021), 1139–1150. https://doi.org/10.1016/S0140-6736(21)00391-3 doi: 10.1016/S0140-6736(21)00391-3
    [5] R. de Boer, A. Perelson, Target cell limited and immune control models of HIV infection: a comparison, J. Theor. Biol., 190 (1998), 201–214. https://doi.org/10.1006/jtbi.1997.0548 doi: 10.1006/jtbi.1997.0548
    [6] N. Dorratoltaj, R. Nikin-Beers, S. M. Ciupe, S. G. Eubank, K. M. Abbas, Multi-scale immunoepidemiological modeling of within-host and between-host HIV dynamics: systematic review of mathematical models, PeerJ, 5 (2017), e3877. https://doi.org/10.7717/peerj.3877 doi: 10.7717/peerj.3877
    [7] J. M. Conway, A. S. Perelson, Post-treatment control of HIV infection, PNAS, 12 (2015), 5467–5472. https://doi.org/10.1073/pnas.1419162112 doi: 10.1073/pnas.1419162112
    [8] A. S. Perelson, P. W. Nelson, Mathematical analysis of HIV-I dynamics in vivo, SIAM Rev., 41 (1999), 3–44. https://doi.org/10.1137/S0036144598335107 doi: 10.1137/S0036144598335107
    [9] M. Nowak, R. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, New York, USA, 2000.
    [10] S. Swanson, A simple model for human immunodeficiency virus based on Erlang's method of stages, SIAM Undergrad. Res. Online, 10 (2017), 65–80. https://doi.org/10.1137/17S015732 doi: 10.1137/17S015732
    [11] H. R. Thieme, C. Castillo-Chavez, How may infection-age-dependent infectivity affect the dynamics of HIV/AIDS, SIAM J. Appl. Math., 53 (1993), 1447–1479. https://doi.org/10.1137/0153068 doi: 10.1137/0153068
    [12] M. Gilchrist, A. Sasaki, Modeling host-parasite coevolution: a nested approach based on mechanistic models, J. Theor. Biol., 218 (2002), 289–308. https://doi.org/10.1006/jtbi.2002.3076 doi: 10.1006/jtbi.2002.3076
    [13] E. Numfor, S. Bhattacharya, M. Martcheva, S. Lenhart, Optimal control in multi-group coupled within-host and between-host models, Electron. J. Differ. Equations, 2016 (2016), 87–117. Available from: https://people.clas.ufl.edu/maia/files/ENumfor-Final.pdf.
    [14] M. Shen, Y. Xiao, L. Rong, L. A. Meyers, Conflict and accord of optimal treatment strategies for HIV infection within and between hosts, Math. Biosci., 309 (2019), 107–117. https://doi.org/10.1016/j.mbs.2019.01.007 doi: 10.1016/j.mbs.2019.01.007
    [15] M. Martcheva, X. Z. Li, Linking immunological and epidemiological dynamics of HIV: the case of super-infection, J. Biol. Dyn., 7 (2013), 161–182. https://doi.org/10.1080/17513758.2013.820358 doi: 10.1080/17513758.2013.820358
    [16] C. Gupta, N. Tuncer, M. Martcheva, A network immuno-epidemiological HIV model, Bull. Math. Biol., 83 (2021), 1–29. https://doi.org/10.1007/s11538-020-00855-3 doi: 10.1007/s11538-020-00855-3
    [17] D. R. Mackintosh, G. T. Stewart, A mathematical model of a heroin epidemic: implications for control policies, J. Epidemiol. Community Health, 33 (1979), 299–304. https://doi.org/10.1136/jech.33.4.299 doi: 10.1136/jech.33.4.299
    [18] T. Phillips, S. Lenhart, W. C. Strickland, A data-driven mathematical model of the heroin and fentanyl epidemic in Tennessee, Bull. Math. Biol., 83 (2021), 27. https://doi.org/10.1007/s11538-021-00925-0 doi: 10.1007/s11538-021-00925-0
    [19] X. C. Duan, H. Cheng, M. Martcheva, S. Yuan, Dynamics of an age structured heroin transmission model with imperfect vaccination, Int. J. Bifurcation Chaos Appl. Sci. Eng., 31 (2021), 2150157. https://doi.org/10.1142/S0218127421501571 doi: 10.1142/S0218127421501571
    [20] X. C. Duan, X. Z. Li, M. Martcheva, Qualitative analysis on a diffusive age-structured heroin transmission model, Nonlinear Anal. Real World Appl., 54 (2020), 103105. https://doi.org/10.1016/j.nonrwa.2020.103105 doi: 10.1016/j.nonrwa.2020.103105
    [21] X. C. Duan, X. Z. Li, M. Martcheva, Dynamics of an age-structured heroin transmission model with vaccination and treatment, Math. Biosci. Eng., 16 (2019), 397–420. https://doi.org/10.3934/mbe.2019019 doi: 10.3934/mbe.2019019
    [22] N. A. Battista, L. B. Pearcy, W. C. Strickland, Modeling the prescription opioid epidemic, Bull. Math. Biol., 81 (2019), 2258–2289. https://doi.org/10.1007/s11538-019-00605-0 doi: 10.1007/s11538-019-00605-0
    [23] N. K. Vaidya, R. M. Ribeiro, A. S. Perelson, A. Kumar, Modeling the effects of morphine on simian immunodeficiency virus dynamics, PLoS Comput. Biol., 12 (2016), e1005127. https://doi.org/10.1371/journal.pcbi.1005127 doi: 10.1371/journal.pcbi.1005127
    [24] J. M Mutua, A. S. Perelson, A. Kumar, N. Vaidya, Modeling the effects of morphine-altered virus-specific antibody responses on HIV/SIV dynamics, Sci. Rep., 9 (2019), 5423. https://doi.org/10.1038/s41598-019-41751-8 doi: 10.1038/s41598-019-41751-8
    [25] N. K. Vaidya, M. Peter, Modeling intracellular delay in within-host HIV dynamics under conditioning of drugs of abuse, Bull. Math. Biol., 83 (2021), 81. https://doi.org/10.1007/s11538-021-00908-1 doi: 10.1007/s11538-021-00908-1
    [26] A. Bloomquist, N. K. Vaidya, Modelling the risk of HIV infection for drug abusers, J. Biol. Dyn., 15 (2021), S81–S104. https://doi.org/10.1080/17513758.2020.1842921 doi: 10.1080/17513758.2020.1842921
    [27] X. C. Duan, X. Z. Li, M. Martcheva, Coinfection dynamics of heroin transmission and HIV infection in a single population, J. Biol. Dyn., 14 (2020), 116–142. https://doi.org/10.1080/17513758.2020.1726516 doi: 10.1080/17513758.2020.1726516
    [28] I. Z. Kiss, J. C. Miller, P. L. Simon, Mathematics of Epidemics on Networks, in Interdisciplinary Applied Mathematics, Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-50806-1
    [29] X. Z. Li, J. Yang, M. Martcheva, Age Structured Epidemic Modeling, in Interdisciplinary Applied Mathematics, Springer, Cham, 2020. https://doi.org/10.1007/978-3-030-42496-1
    [30] J. Yang, T. Kuniya, X. Luo, Competitive exclusion in a multi-strain SIS epidemic model on complex networks, Electron. J. Differ. Equations, 2019 (2019), 1–30. Available from: https://www.researchgate.net/publication/330370854.
    [31] P. Wu, H. Zhao, Dynamics of an hiv infection model with two infection routes and evolutionary competition between two viral strains, Appl. Math. Modell., 84 (2020), 240–264. https://doi.org/10.1016/j.apm.2020.03.040 doi: 10.1016/j.apm.2020.03.040
    [32] A. L. Barabási, Network Science, Computer Science, Cambridge University Press, 2016.
    [33] Z. Jin, G. Sun, H. Zhu, Epidemic models for complex networks with demographics, Math. Biosci. Eng., 11 (2014), 1295–1317. https://doi.org/10.3934/mbe.2014.11.1295 doi: 10.3934/mbe.2014.11.1295
    [34] R. Rothenberg, HIV transmission networks, Curr. Opin. HIV AIDS, 4 (2009), 260–265. https://doi.org/10.1097/COH.0b013e32832c7cfc doi: 10.1097/COH.0b013e32832c7cfc
    [35] A. Lange, N. Ferguson, Antigenic diversity, transmission mechanisms, and the evolution of pathogens, PLoS Comput. Biol., 5 (2009), e1000536. https://doi.org/10.1371/journal.pcbi.1000536 doi: 10.1371/journal.pcbi.1000536
    [36] M. Martcheva, An Introduction to Mathematical Epidemiology, in Texts in Applied Mathematics, Springer, New York, 2015. https://doi.org/10.1007/978-1-4899-7612-3
    [37] M. A. Gilchrist, D. Coombs, Evolution of virulence: interdependence, constraints, and selection using nested models, Theor. Popul. Biol., 69 (2006), 145–153. https://doi.org/10.1016/j.tpb.2005.07.002 doi: 10.1016/j.tpb.2005.07.002
    [38] C. Gupta, N. Tuncer, M. Martcheva, Immuno-epidemiological co-affection model of hiv infection and opioid addiction, Math. Biosci. Eng., 19 (2022), 3636–3672. https://doi.org/10.3934/mbe.2022168 doi: 10.3934/mbe.2022168
    [39] R. Nsubuga, R. White, B. Mayanja, L. A Shafer, Estimation of the hiv basic reproduction number in rural south west Uganda: 1991–2008, PLoS One, 9 (2014), e83778. https://doi.org/10.1371/journal.pone.0083778 doi: 10.1371/journal.pone.0083778
    [40] Nearly one in three people know someone addicted to opioids; More than half of millennials believe it is easy to get illegal opioids, 2022. Available from: https://psychiatry.org/news-room/news-releases/nearly-one-in-three-people-know-someone-addicted-t.
    [41] C. O. Cunningham, Opioids and hiv infection: from pain management to addiction treatment, Top. Antivir. Med., 25 (2018), 143–146.
    [42] K. A. Hoffman, C. Vilsaint, J. F. Kelly, Recovery from opioid problems in the us population: prevalence, pathways, and psychological well-being, J. Addict. Med., 14 (2020), 207–216. https://doi.org/10.1097/ADM.0000000000000561 doi: 10.1097/ADM.0000000000000561
    [43] Heroin drugfacts, 2022. Available from: https://nida.nih.gov/publications/drugfacts/heroin.
    [44] U.S. overdose deaths in 2021 increased half as much as in 2020 – but are still up 15 percent, 2022. Available from: https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2022/202205.htm.
    [45] HIV basic statistics, 2022. Available from: https://www.cdc.gov/hiv/basics/statistics.html.
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1701) PDF downloads(87) Cited by(0)

Article outline

Figures and Tables

Figures(4)  /  Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog