Antiviral resistance during pandemic influenza: implications for stockpiling and drug use

Abstract

Background: The anticipated extent of antiviral use during an influenza pandemic can have adverse consequences for the development of drug resistance and rationing of limited stockpiles. The strategic use of drugs is therefore a major public health concern in planning for effective pandemic responses.

Methods: We employed a mathematical model that includes both sensitive and resistant strains of a virus with pandemic potential, and applies antiviral drugs for treatment of clinical infections. Using estimated parameters in the published literature, the model was simulated for various sizes of stockpiles to evaluate the outcome of different antiviral strategies.

Results: We demonstrated that the emergence of highly transmissible resistant strains has no significant impact on the use of available stockpiles if treatment is maintained at low levels or the reproduction number of the sensitive strain is sufficiently high. However, moderate to high treatment levels can result in a more rapid depletion of stockpiles, leading to run-out, by promoting wide-spread drug resistance. We applied an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. Our results show that if high treatment levels are enforced too early during the outbreak, a second wave of infections can potentially occur with a substantially larger magnitude. However, a timely implementation of wide-scale treatment can prevent resistance spread in the population, and minimize the final size of the pandemic.

Conclusion: Our results reveal that conservative treatment levels during the early stages of the outbreak, followed by a timely increase in the scale of drug-use, will offer an effective strategy to manage drug resistance in the population and avoid run-out. For a 1918-like strain, the findings suggest that pandemic plans should consider stockpiling antiviral drugs to cover at least 20% of the population.

Figure 1

Model structure. Model diagram for the progression of disease and development of drug-resistance during treatment of infected individuals.

Figure 2

Required antiviral stockpile and ratio of the total clinical infections. Required stockpile of antiviral drugs (relative to S₀) as a function of the treatment level for (a) R₀ = 1.5; and (b) R₀ = 2.5. Ratio of the total number of clinical infections to S₀ as a function of the treatment level for: (c) R₀ = 1.5; and (d) R₀ = 2.5. Solid curves correspond to the case where resistance is absent, and dashed curves represent the scenario in which resistant viruses with HTF are present.

Figure 3

Unlimited supply of antiviral drugs. Required stockpile of antiviral drugs (relative to S₀) as a function of R₀ with a constant treatment level of: (a) 20%; (b) 40%; and (c) 60%. Ratio of the total number of clinical infections to S₀ as a function of R₀ with a constant treatment level of: (d) 20%; (e) 40%; and (f) 60%. Solid curves correspond to the case where resistance is absent, and dashed curves represent the scenario in which resistant viruses with HTF are present.

Figure 4

Limited supply of antiviral drugs. Antiviral use of an initial 12% stockpile (relative to S₀) as a function of R₀ with a constant treatment level of: (a) 20%; (b) 40%; and (c) 60%. Ratio of the total number of clinical infections to S₀ as a function of R₀ with a constant treatment level of: (d) 20%; (e) 40%; and (f) 60%. Solid curves correspond to the case where resistance is absent, and dashed curves represent the scenario in which resistant viruses with HTF are present.

Figure 5

Final size of infections with adaptive treatment strategy. The effect of changing treatment level during the outbreak on the total number of clinical infections caused by all strains, with various sizes of stockpile and R₀ = 2. Simulations were seeded with an initial treatment level of: (a) 0% without resistance; (b) 0% with resistance; (c) 25% without resistance; (d) 25% with resistance, and then changed to 80% at the time displayed on the vertical axis (corresponding to the time-course of the outbreak). The color bars illustrate the ratio of the total number of clinical infection to S₀ due to all strains. Run-out occurs in the regions consisting of the origin and delimited by the solid curves.

Figure 6

Time-courses of the outbreak with adaptive treatment strategy. The effect of changing treatment level during the outbreak on the time course of clinical infections with R₀ = 2. Simulations were seeded with an initial treatment level 25%, and then changed to 80% at: (a) day 40 with stockpile of size 8.5% (relative to S₀); (b) day 40 with stockpile of size 12% (relative to S₀); (c) day 40 with adequate drug supply; (d) day 50 with adequate drug supply. Solid curves show the time courses for the ratio of the clinical infections to S₀ caused by all strains, and dashed curves represent those of only resistant infections.