CMAJ • November 10, 2009; 181 (10). First published October 13, 2009; doi:10.1503/cmaj.091641
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Research

Modelling mitigation strategies for pandemic (H1N1) 2009

Marija Zivkovic Gojovic, MSc, Beate Sander, RN MEcDev, David Fisman, MD MPH, Murray D. Krahn, MD MSc and Chris T. Bauch, PhD

From the Toronto Health Economics and Technology Assessment Collaborative (Gojovic, Sander, Krahn, Bauch); the Department of Mathematics and Statistics (Gojovic), York University; the Division of Clinical Decision-Making and Health Care Research (Sander, Krahn), University Health Network; the Ontario Agency for Health Protection and Promotion (Fisman); Research Institute of the Hospital for Sick Children (Fisman); Faculty of Pharmacy (Krahn), the Departments of Health Policy, Management and Evaluation (Sander, Krahn), Medicine (Krahn) and Epidemiology (Fisman), Dalla Lana School of Public Health, University of Toronto, Toronto, Ont.; and the Department of Mathematics and Statistics (Bauch), University of Guelph, Guelph, Ont.

Correspondence to: Dr. Marija Zivkovic Gojovic, Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, 144 College St., Toronto ON M5S 3M2; fax 416 946-3719; mgojovic{at}uhnres.utoronto.ca

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.


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Highlights
Can. Med. Assoc. J. 2009 181: 657. [Full Text] [PDF]