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Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response

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  • Hana M Dobrovolny
  • Micaela B Reddy
  • Mohamed A Kamal
  • Craig R Rayner
  • Catherine A A Beauchemin

Abstract

The role of the host immune response in determining the severity and duration of an influenza infection is still unclear. In order to identify severity factors and more accurately predict the course of an influenza infection within a human host, an understanding of the impact of host factors on the infection process is required. Despite the lack of sufficiently diverse experimental data describing the time course of the various immune response components, published mathematical models were constructed from limited human or animal data using various strategies and simplifying assumptions. To assess the validity of these models, we assemble previously published experimental data of the dynamics and role of cytotoxic T lymphocytes, antibodies, and interferon and determined qualitative key features of their effect that should be captured by mathematical models. We test these existing models by confronting them with experimental data and find that no single model agrees completely with the variety of influenza viral kinetics responses observed experimentally when various immune response components are suppressed. Our analysis highlights the strong and weak points of each mathematical model and highlights areas where additional experimental data could elucidate specific mechanisms, constrain model design, and complete our understanding of the immune response to influenza.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0057088
    DOI: 10.1371/journal.pone.0057088
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    References listed on IDEAS

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    2. S Robinson, 2008. "Conceptual modelling for simulation Part I: definition and requirements," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 278-290, March.
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    1. 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.
    2. Van Kinh Nguyen & Frank Klawonn & Rafael Mikolajczyk & Esteban A Hernandez-Vargas, 2016. "Analysis of Practical Identifiability of a Viral Infection Model," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-16, December.
    3. Micaela B Reddy & Kuo-Hsiung Yang & Gauri Rao & Craig R Rayner & Jing Nie & Chandrasena Pamulapati & Bindumadhav M Marathe & Alan Forrest & Elena A Govorkova, 2015. "Oseltamivir Population Pharmacokinetics in the Ferret: Model Application for Pharmacokinetic/Pharmacodynamic Study Design," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    4. Ma, Yuanlin & Yu, Xingwang, 2020. "The effect of environmental noise on threshold dynamics for a stochastic viral infection model with two modes of transmission and immune impairment," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    5. 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.
    6. Pablo Boullosa & Adrián Garea & Iván Area & Juan J. Nieto & Jorge Mira, 2022. "Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-15, July.

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