IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v133y2017icp206-222.html
   My bibliography  Save this article

The logistic growth model as an approximating model for viral load measurements of influenza A virus

Author

Listed:
  • Arenas, Abbiana R.
  • Thackar, Neil B.
  • Haskell, Evan C.

Abstract

Detailed kinetic models of viral replication have led to greater understanding of disease progression and the effects of therapy. Viral load is important as a driver of the immune response to the viral infection and in determining the infectiousness of an infected individual. However in many cases when examining the immune response or spread of infection, it may be sufficient to have a more parsimonious model of viral load than the detailed kinetic models. Here we review properties of detailed kinetic models of Influenza A virus and discuss the use of a logistic growth model to approximate viral load. We make application of the tools of identifiability analysis and model selection to assess the logistic growth model as a proxy for viral load. We find that the parameters of the logistic growth model can be related to the parameters of a detailed viral kinetic model, the logistic growth model makes a strong fit to viral load data with a small number of parameters, and that these parameters can be reliably identified from viral load data generated by a detailed kinetic model.

Suggested Citation

  • Arenas, Abbiana R. & Thackar, Neil B. & Haskell, Evan C., 2017. "The logistic growth model as an approximating model for viral load measurements of influenza A virus," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 133(C), pages 206-222.
  • Handle: RePEc:eee:matcom:v:133:y:2017:i:c:p:206-222
    DOI: 10.1016/j.matcom.2016.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475416301951
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2016.10.002?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. Christina E. Mills & James M. Robins & Marc Lipsitch, 2004. "Transmissibility of 1918 pandemic influenza," Nature, Nature, vol. 432(7019), pages 904-906, December.
    2. Hana M Dobrovolny & Micaela B Reddy & Mohamed A Kamal & Craig R Rayner & Catherine A A Beauchemin, 2013. "Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-20, February.
    3. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    4. Erik A. Karlsson & Victoria A. Meliopoulos & Chandra Savage & Brandi Livingston & Andrew Mehle & Stacey Schultz-Cherry, 2015. "Visualizing real-time influenza virus infection, transmission and protection in ferrets," Nature Communications, Nature, vol. 6(1), pages 1-10, May.
    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. Lawrence M. Wein & Michael P. Atkinson, 2009. "Assessing Infection Control Measures for Pandemic Influenza," Risk Analysis, John Wiley & Sons, vol. 29(7), pages 949-962, July.
    2. Savachkin, Alex & Uribe, Andrés, 2012. "Dynamic redistribution of mitigation resources during influenza pandemics," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 33-45.
    3. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    4. Ali Ekici & Pınar Keskinocak & Julie L. Swann, 2014. "Modeling Influenza Pandemic and Planning Food Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 11-27, February.
    5. Elnaz Karimi & Ketra Schmitt & Ali Akgunduz, 2015. "Effect of individual protective behaviors on influenza transmission: an agent-based model," Health Care Management Science, Springer, vol. 18(3), pages 318-333, September.
    6. Carrasco, L R & Lee, V J & Chen, M I & Matchar, D B & Thompson, J P & Cook, A R, 2011. "Strategies for antiviral stockpiling for future influenza pandemics: a global epidemic-economic perspective," MPRA Paper 57763, University Library of Munich, Germany.
    7. Nedialko B Dimitrov & Sebastian Goll & Nathaniel Hupert & Babak Pourbohloul & Lauren Ancel Meyers, 2011. "Optimizing Tactics for Use of the U.S. Antiviral Strategic National Stockpile for Pandemic Influenza," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    8. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    9. Jeremy Hadidjojo & Siew Ann Cheong, 2011. "Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-10, July.
    10. Tamer Edirne & Dilek Avci & Burçak Dagkara & Muslum Aslan, 2011. "Knowledge and anticipated attitudes of the community about bird flu outbreak in Turkey, 2007–2008: a survey-based descriptive study," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(2), pages 163-168, April.
    11. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    12. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    13. Aditya Goenka & Lin Liu, 2012. "Infectious diseases and endogenous fluctuations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 50(1), pages 125-149, May.
    14. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    15. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    16. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    17. Linus Nyiwul, 2021. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19," Studies in Microeconomics, , vol. 9(2), pages 283-305, December.
    18. Zhongqiang Bai & Juanle Wang & Mingming Wang & Mengxu Gao & Jiulin Sun, 2018. "Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China," Sustainability, MDPI, vol. 10(5), pages 1-15, April.
    19. Abraham J. Arenas & Gilberto González-Parra & Jhon J. Naranjo & Myladis Cogollo & Nicolás De La Espriella, 2021. "Mathematical Analysis and Numerical Solution of a Model of HIV with a Discrete Time Delay," Mathematics, MDPI, vol. 9(3), pages 1-21, January.
    20. James Truscott & Neil M Ferguson, 2012. "Evaluating the Adequacy of Gravity Models as a Description of Human Mobility for Epidemic Modelling," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.

    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:matcom:v:133:y:2017:i:c:p:206-222. 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: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.