Antiviral treatment for pandemic influenza: Assessing potential repercussions using a seasonally forced SIR model

• Published in: Journal of theoretical biology 2011-11, Vol.289, p.259-268
• Main Authors: Towers, S., Vogt Geisse, K., Zheng, Y., Feng, Z.
• Format: Article
• Language: English
• Subjects: Antiviral Agents - adverse effects; Antiviral Agents - therapeutic use; Antiviral treatment model; autumn; control methods; disease models; Disease Susceptibility; drug resistance; drug therapy; Epidemic model; Epidemic size; health policy; Humans; influenza; Influenza A Virus, H1N1 Subtype; Influenza, Human - drug therapy; Influenza, Human - epidemiology; Influenza, Human - transmission; Models, Biological; pandemic; Pandemic influenza model; Pandemics - prevention & control; Seasonally forced transmission; Seasons; vaccines
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2011.08.011

When resources are limited, measures to control an incipient influenza pandemic must be carefully considered. Because several months are needed to mass-produce vaccines once a new pandemic strain has been identified, antiviral drugs are often considered the first line of defense in a pandemic situation. Here we use an SIR disease model with periodic transmission rate to assess the efficacy of control strategies via antiviral drug treatment during an outbreak of pandemic influenza. We show that in some situations, and independent of drug-resistance effects, antiviral treatment can have a detrimental impact on the final size of the pandemic. Antiviral treatment also has the potential to increase the size of the major peak of the pandemic, and cause it to occur earlier than it would have if treatment were not used. Our studies suggest that when a disease exhibits periodic patterns in transmission, decisions of public health policy will be particularly important as to how control measures such as drug treatment should be implemented, and to what end (i.e.; towards immediate control of a current epidemic peak, or towards potential delay and/or reduction of an anticipated autumn peak). ► We model pandemic influenza using a disease model with a seasonal transmission rate. ► We assess the impact of antiviral treatment programs on the size of the pandemic. ► We find that antiviral treatment has the potential to make pandemics much larger. ► The underlying cause of this phenomenon is the seasonality of influenza. ► Our model results can be used towards more effective pandemic response planning.