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

Overview of attention for article published in PLOS ONE, February 2013
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Title
Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0057088
Pubmed ID
Authors

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.

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Geographical breakdown

Country Count As %
Germany 2 2%
United Kingdom 1 1%
India 1 1%
Australia 1 1%
Unknown 85 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 22%
Student > Ph. D. Student 18 20%
Student > Master 7 8%
Professor 6 7%
Student > Bachelor 6 7%
Other 19 21%
Unknown 14 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 23%
Mathematics 11 12%
Medicine and Dentistry 7 8%
Immunology and Microbiology 6 7%
Engineering 6 7%
Other 19 21%
Unknown 20 22%