Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives

Abstract

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R(0) and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic.

Figure 1. Magnitude and duration of responses to previous severe mortality outbreaks.

Estimates of the reduction in the reproduction number and the duration of interventions during responses to the SARS outbreak in 2003 by country (open triangles) and during the 1918 influenza pandemic in cities in the USA (closed circles). A transmission reduction of 0% reflects an intervention which was estimated to have no effect on transmission.

Figure 2. Effect of timing and strength of interventions on outcome for long-term interventions.

A, C: Prevalence of infectious cases under different control scenarios (dotted line indicates unconstrained epidemic). B, D Effect of interventions on total number of cases (solid bars), peak prevalence (striped bars), and time until final case recovers (diamonds). A, B intervention commences week 3, 5, 6 and 7 with a 33.3% reduction in transmission. C, D intervention commences week 5 with 11.1%, 22.2%, 33.3% and 44.4% reduction in transmission.

Figure 3. Effect of timing and strength of interventions on outcome for an intervention of 12 weeks.

A, C: Prevalence of infectious cases under different control scenarios with dotted line indicating unconstrained epidemic. B, D Effect of interventions on total number of cases (solid bars), peak prevalence (striped bars), and time until final case recovers (diamonds). A, B intervention commences week 3, 5, 6 and 7 with a 33.3% reduction in transmission. C, D intervention commences week 5 with 11.1%, 22.2%, 33.3% and 44.4% reduction in transmission.

Figure 4. Comparison of intervention strategies which ‘buy time’ until a strain-specific vaccine is available 6 months into the epidemic and contain symptomatic cases to utilize a stockpile of treatments for 25% of the population.

A Uncontrolled epidemic (black dotted curve) and epidemic curves for five different strategies, starting at different times: T ₁ = 0, 2, 4, 6, or 7 weeks into the epidemic. The required reductions in transmission are  = 32%, 34%, 36%, 37% and 49%. B Peak prevalence (solid curve) and costs of interventions calculated as (dashed curve), in relation to the time of commencement of intervention. C Excess number of cases for the five strategies if the parameters of the epidemic are different to those for which these interventions were designed: the availability of a strain specific vaccine is delayed until 8 months (black), transmission has been overestimated and R ₀ = 1.7 (dark grey), or transmission has been underestimated and R ₀ = 2 (light grey).

Figure 5. Addition of a pre-pandemic vaccine for 10% of the population.

Comparison of intervention strategies which ‘buy time’ until a strain-specific vaccine is available 6 months into the epidemic and contain symptomatic cases to utilize a stockpile of treatments for 25% of the population when 10% of the population are vaccinated with a vaccine which reduces susceptibility and infectiousness by30%. A Uncontrolled epidemic (black dotted curve) and epidemic curves for five different strategies, starting at different times: T ₁ = 0, 2, 4, 6, or 7 weeks into the epidemic. The required reductions in transmission are  = 28%, 30%, 31%, 32% and 33%. B Peak prevalence (solid curve) and costs of interventions calculated as (dashed curve), in relation to the time of commencement of intervention. C Excess number of cases for the five strategies if the parameters of the epidemic are different to those for which these interventions were designed: the pre-pandemic vaccine is less effective (black), transmission has been overestimated and R ₀ = 1.7 (dark grey), or transmission has been underestimated and R ₀ = 2 (light grey).