title: Modelling mitigation strategies for pandemic (H1N1) 2009.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Research Institute of the Hospital for Sick Children, Toronto, ON. mgojovic@uhnres.utoronto.ca
authors.forename: Marija Zivkovic
authors.initials: MZ
authors.lastname: Gojovic
authors.forename: Beate
authors.initials: B
authors.lastname: Sander
authors.forename: David
authors.initials: D
authors.lastname: Fisman
authors.forename: Murray D
authors.initials: MD
authors.lastname: Krahn
authors.forename: Chris T
authors.initials: CT
authors.lastname: Bauch
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003140:Communicable Disease Control / Q000379:methods; D003657:Decision Making; D004196:Disease Outbreaks / Q000517:prevention & control*; D005260:Female; D006801:Humans; D017589:Immunization Programs / Q000458:organization & administration*; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007252:Influenza Vaccines / Q000008:administration & dosage*; D007251:Influenza, Human / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009864:Ontario; D011322:Primary Prevention / Q000458:organization & administration; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014505:Urban Population; D055815:Young Adult
chemical_list: D007252:Influenza Vaccines