IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v93y2014icp52-62.html
   My bibliography  Save this article

The arrested immunity hypothesis in an immunoepidemiological model of Chlamydia transmission

Author

Listed:
  • Vickers, David M.
  • Osgood, Nathaniel D.

Abstract

For curable infectious diseases, public health strategies such as treatment can effectively shorten an individual’s infectious period, and thus limit their role in transmission. However, because treatment effectively eliminates antigen impingement, these types of control strategies may also paradoxically impair the development of adaptive immune responses. For sexually transmitted Chlamydia trachomatis infections, this latter effect has been coined the arrested immunity hypothesis, and is discussed to carry significant epidemiological implications for those individuals who return to similar sexual networks with similar sexual behavior. Here, we examine the effect of antibiotic treatment on the spread of Chlamydia infection through a simple immunoepidemiological framework that characterizes the population as a collection of dynamically evolving individuals in small, paradigmatic networks. Within each individual there is an explicit representation of pathogen replication, accumulation and persistence of an immune response, followed by a gradual waning of that response once the infection is cleared. Individuals are then nested in networks, allowing the variability in the life history of their infection to be functions of both individual immune dynamics as well as their position in the network. Model results suggest that the timing and coverage of treatment are important contributors to the development of immunity and reinfection. In particular, the impact of treatment on the spread of infection between individuals can be beneficial, have no effect, or be deleterious depending on who is treated and when. Although we use sexually transmitted Chlamydia infection as an example, the observed results arise endogenously from a basic model structure, and thus warrant consideration to understanding the interaction of infection, treatment, and spread of other infectious diseases.

Suggested Citation

  • Vickers, David M. & Osgood, Nathaniel D., 2014. "The arrested immunity hypothesis in an immunoepidemiological model of Chlamydia transmission," Theoretical Population Biology, Elsevier, vol. 93(C), pages 52-62.
    • Handle: RePEc:eee:thpobi:v:93:y:2014:i:c:p:52-62
    DOI: 10.1016/j.tpb.2014.01.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580914000148
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tpb.2014.01.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Heffernan, J.M. & Keeling, M.J., 2008. "An in-host model of acute infection: Measles as a case study," Theoretical Population Biology, Elsevier, vol. 73(1), pages 134-147.
    2. Thomas House & Matt J Keeling, 2010. "The Impact of Contact Tracing in Clustered Populations," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-9, March.
    3. Sterman, J.D., 2006. "Learning from evidence in a complex world," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 505-514.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    2. Sridharan, Sanjeev & Jones, Bobby & Caudill, Barry & Nakaima, April, 2016. "Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach," Evaluation and Program Planning, Elsevier, vol. 58(C), pages 88-97.
    3. Wenjing Luo & Zhi Qiu & Yurika Yokoyama & Shengyuan Zheng, 2022. "Decision-Making Mechanism of Joint Activities for the Elderly and Children in Integrated Welfare Facilities: A Discussion Based on “Motivation–Constraint” Interaction Model," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    4. Kimberly M. Thompson & Radboud J. Duintjer Tebbens, 2006. "Retrospective Cost‐Effectiveness Analyses for Polio Vaccination in the United States," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1423-1440, December.
    5. Regina Veckalne & Tatjana Tambovceva, 2022. "The Role of Digital Transformation in Education in Promoting Sustainable Development," Virtual Economics, The London Academy of Science and Business, vol. 5(4), pages 65-86, December.
    6. repec:mpr:mprres:6558 is not listed on IDEAS
    7. Louise Freebairn & Jo-An Atkinson & Nathaniel D Osgood & Paul M Kelly & Geoff McDonnell & Lucie Rychetnik, 2019. "Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-27, June.
    8. de Espíndola, Aquino L. & Girardi, Daniel & Penna, T.J.P. & Bauch, Chris T. & Troca Cabella, Brenno C. & Martinez, Alexandre Souto, 2014. "An antibiotic protocol to minimize emergence of drug-resistant tuberculosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 80-92.
    9. Margaret Hargreaves & Diane Paulsell, 2009. "Evaluating Systems Change Efforts to Support Evidence-Based Home Visiting: Concepts and Methods," Mathematica Policy Research Reports c24fc47e9c744b52a00e2137c, Mathematica Policy Research.
    10. Yang, Y. & Lin, J. & Liu, G. & Zhou, L., 2021. "The behavioural causes of bullwhip effect in supply chains: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 236(C).
    11. Brice Dattée & James Barlow, 2017. "Multilevel Organizational Adaptation: Scale Invariance in the Scottish Healthcare System," Organization Science, INFORMS, vol. 28(2), pages 301-319, April.
    12. van Ackere, Ann & Schulz, Peter J., 2020. "Explaining vaccination decisions: A system dynamics model of the interaction between epidemiological and behavioural factors," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    13. Kwamina Ewur Banson & Daniel Kwasi Asare & Fidelis Doodaa Dery & Kwadwo Boakye & Akudugu Boniface & Moses Asamoah & Lourees Esi Awotwe, 2020. "Impact of Fall Armyworm on Farmer’s Maize: Systemic Approach," Systemic Practice and Action Research, Springer, vol. 33(2), pages 237-264, April.
    14. Hassmiller Lich, Kristen & Urban, Jennifer Brown & Frerichs, Leah & Dave, Gaurav, 2017. "Extending systems thinking in planning and evaluation using group concept mapping and system dynamics to tackle complex problems," Evaluation and Program Planning, Elsevier, vol. 60(C), pages 254-264.
    15. Yuya Kajikawa, 2022. "Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5571-5585, September.
    16. Miles M. Yang & Hong Jiang & Michael Shayne Gary, 2016. "Challenging learning goals improve performance in dynamically complex microworld simulations," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 204-232, July.
    17. Boelsen-Robinson, Tara & Blake, Miranda R. & Brown, Andrew D. & Huse, Oliver & Palermo, Claire & George, Neetu A. & Peeters, Anna, 2021. "Mapping factors associated with a successful shift towards healthier food retail in community-based organisations: A systems approach," Food Policy, Elsevier, vol. 101(C).
    18. Brynle Owen & Andrew D Brown & Jill Kuhlberg & Lynne Millar & Melanie Nichols & Christina Economos & Steven Allender, 2018. "Understanding a successful obesity prevention initiative in children under 5 from a systems perspective," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-10, March.
    19. Negar Darabi & Niyousha Hosseinichimeh, 2020. "System dynamics modeling in health and medicine: a systematic literature review," System Dynamics Review, System Dynamics Society, vol. 36(1), pages 29-73, January.
    20. Oosterbroek, Bram & de Kraker, Joop & Huynen, Maud M.T.E. & Martens, Pim, 2016. "Assessing ecosystem impacts on health: A tool review," Ecosystem Services, Elsevier, vol. 17(C), pages 237-254.
    21. Watinee Kunpeuk & Pard Teekasap & Hathairat Kosiyaporn & Sataporn Julchoo & Mathudara Phaiyarom & Pigunkaew Sinam & Nareerut Pudpong & Rapeepong Suphanchaimat, 2020. "Understanding the Problem of Access to Public Health Insurance Schemes among Cross-Border Migrants in Thailand through Systems Thinking," IJERPH, MDPI, vol. 17(14), pages 1-19, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:thpobi:v:93:y:2014:i:c:p:52-62. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.