ArticlePloS one2013
Assessing mathematical models of influenza infections using features of the immune response.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Review
- Modeling viral shedding and symptom outcomes in oseltamivir-treated experimental influenza infection.PloS one · 2026Article
- Mathematical Modeling of Influenza Dynamics: Integrating Seasonality and Gradual Waning Immunity.Bulletin of mathematical biology · 2025Article
- Perspectives on physics-based one-dimensional modeling of lung physiology.Frontiers in physiology · 2025Article
- Mathematical Model of Influenza Infection Suggests JAK-STAT Activity Drives Severe Pathologies in Juvenile Mice.Frontiers in applied mathematics and statistics · 2025Article
- Influenza virus shedding and symptoms: Dynamics and implications from a multiseason household transmission study.PNAS nexus · 2024Article
- Modeling the transmission mitigation impact of testing for infectious diseases.Science advances · 2024Article
- Mathematical model calibrated toiScience · 2024Article
- Respiratory Viral Coinfections: Insights into Epidemiology, Immune Response, Pathology, and Clinical Outcomes.Pathogens (Basel, Switzerland) · 2024Review
- Modeling the Transmission Mitigation Impact of Testing for Infectious Diseases.medRxiv : the preprint server for health sciences · 2024Article
- Mathematical Modeling of Virus-Mediated Syncytia Formation: Past Successes and Future Directions.Results and problems in cell differentiation · 2024Review
- Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.Mathematical biosciences · 2023Article
- Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity.eLife · 2021Article
- Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Advanced drug delivery reviews · 2021Review
- A Prototype QSP Model of the Immune Response to SARS-CoV-2 for Community Development.CPT: pharmacometrics & systems pharmacology · 2021Review
- Dynamical characterization of antiviral effects in COVID-19.Annual reviews in control · 2021Article
- SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial?Journal of medical virology · 2020Article
- Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections.Epidemiologia (Basel, Switzerland) · 2020Article
- Article
- A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract.PLoS computational biology · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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