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

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  • 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
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    1. 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.
    2. 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.
    3. Christina E. Mills & James M. Robins & Marc Lipsitch, 2004. "Transmissibility of 1918 pandemic influenza," Nature, Nature, vol. 432(7019), pages 904-906, December.
    4. 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.
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