Modelling mitigation strategies for pandemic (H1N1) 2009

Background

The 2009 influenza A (H1N1) pandemic has required decision-makers to act in the face of substantial uncertainties. Simulation models can be used to project the effectiveness of mitigation strategies, but the choice of the best scenario may change depending on model assumptions and uncertainties.

Methods

We developed a simulation model of a pandemic (H1N1) 2009 outbreak in a structured population using demographic data from a medium-sized city in Ontario and epidemiologic influenza pandemic data. We projected the attack rate under different combinations of vaccination, school closure and antiviral drug strategies (with corresponding “trigger” conditions). To assess the impact of epidemiologic and program uncertainty, we used “combinatorial uncertainty analysis.” This permitted us to identify the general features of public health response programs that resulted in the lowest attack rates.

Results

Delays in vaccination of 30 days or more reduced the effectiveness of vaccination in lowering the attack rate. However, pre-existing immunity in 15% or more of the population kept the attack rates low, even if the whole population was not vaccinated or vaccination was delayed. School closure was effective in reducing the attack rate, especially if applied early in the outbreak, but this is not necessary if vaccine is available early or if pre-existing immunity is strong.

Interpretation

Early action, especially rapid vaccine deployment, is disproportionately effective in reducing the attack rate. This finding is particularly important given the early appearance of pandemic (H1N1) 2009 in many schools in September 2009.Jurisdictions in the northern hemisphere are bracing for a “fall wave” of pandemic (H1N1) 2009.^1–3 Decision-makers face uncertainty, not just with respect to epidemiologic characteristics of the virus,⁴ but also program uncertainties related to feasibility, timeliness and effectiveness of mitigation strategies.⁵ Policy decisions must be made against this backdrop of uncertainty. However, the effectiveness of any mitigation strategy generally depends on the epidemiologic characteristics of the pathogen as well as the other mitigation strategies adopted. Mathematical models can project strategy effectiveness under hypothetical epidemiologic and program scenarios.^6–12 In the case of pandemic influenza, models have been used to assess the effectiveness of school closure⁷ and optimal use of antiviral drug^6,9,10 and vaccination strategies.⁸ However, model projections can be sensitive to input parameter values; thus, data uncertainty is an issue.¹³ Uncertainty analysis can help address the impact of uncertainties on model predictions but is often underutilized.¹³In this article, we present a simulation model of pandemic influenza transmission and mitigation in a population. This model projects the overall attack rate (percentage of people infected) during an outbreak. We introduce a formal method of uncertainty analysis that has not previously been applied to pandemic influenza, and we use this method to assess the impact of epidemiologic and program uncertainties. The model is intended to address the following policy questions that have been raised during the 2009 influenza pandemic: What is the impact of delayed vaccine delivery on attack rates? Can attack rates be substantially reduced without closing schools? What is the impact of pre-existing immunity from spring and summer 2009? We addressed these questions using a simulation model that projects the impact of vaccination, school closure and antiviral drug treatment strategies on attack rates.