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Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
Published on: January 20, 2017
Population modeling of influenza A/H1N1 virus kinetics and symptom dynamics
Laetitia Canini1, Fabrice Carrat
1Epidémiologie, Systèmes d'Information, Modélisation, UMR-S 707, Faculté de Médecine Saint Antoine, 27 Rue Chaligny, 75571 Paris, Cedex 12, France.
Abstract:
Influenza virus kinetics (VK) is used as a surrogate of infectiousness, while the natural history of influenza is described by symptom dynamics (SD). We used an original virus kinetics/symptom dynamics (VKSD) model to characterize human influenza virus infection and illness, based on a population approach. We combined structural equations and a statistical model to describe intra- and interindividual variability. The structural equations described influenza based on the target epithelial cells, the virus, the innate host response, and systemic symptoms. The model was fitted to individual VK and SD data obtained from 44 volunteers experimentally challenged with influenza A/H1N1 virus. Infection and illness parameters were calculated from best-fitted model estimates. We predicted that the cytokine level and NK cell activity would peak at days 2.2 and 4.2 after inoculation, respectively. Infectiousness, measured as the area under the VK curve above a viral titer threshold, lasted between 7.0 and 1.3 days and was 15 times lower in participants without systemic symptoms than in those with systemic symptoms (P < 0.001). The latent period, defined as the time between inoculation and infectiousness, varied from 0.7 to 1.9 days. The incubation period, defined as the time from inoculation to first symptoms, varied from 1.0 to 2.4 days. Our approach extends previous work by including the innate response and providing realistic estimates of infection and illness parameters, taking into account the strong interindividual variability. This approach could help to optimize studies of influenza VK and SD and to predict the effect of antivirals on infectiousness and symptoms.
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