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

Abbiana R. Arenas, Neil B. Thackar and Evan C. Haskell

Mathematics and Computers in Simulation (MATCOM), 2017, vol. 133, issue C, 206-222

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.

Keywords: Influenza; Logistic growth; Model selection; Identifiability analysis (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:133:y:2017:i:c:p:206-222

DOI: 10.1016/j.matcom.2016.10.002

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