Modelling mitigation strategies for pandemic (H1N1) 2009

¹ Toronto Health Economics and Technology Assessment Collaborative; the Department of Mathematics and Statistics, York University, Toronto, Ont.
² Toronto Health Economics and Technology Assessment Collaborative; the Division of Clinical Decision-Making and Health Care Research, University Health Network; the Department of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ont.
³ Ontario Agency for Health Protection and Promotion; Research Institute of the Hospital for Sick Children; the Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ont.
⁴ Toronto Health Economics and Technology Assessment Collaborative; the Division of Clinical Decision-Making and Health Care Research, University Health Network; Faculty of Pharmacy, the Departments of Health Policy, Management and Evaluation, and Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ont.
⁵ Toronto Health Economics and Technology Assessment Collaborative; the Department of Mathematics and Statistics, University of Guelph, Guelph, Ont.

	Abstract

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.