title: Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything.
abstract: BACKGROUND
  Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.
  
  
  METHODS
  We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.
  
  
  RESULTS
  The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.
  
  
  CONCLUSIONS
  Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.
authors.affiliation: Division of Mathematical Modeling, University of British Columbia Centre for Disease Control, 655 West 12th Avenue, V5Z 4R4 Vancouver, British Columbia, Canada.
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Conway
authors.forename: Ashleigh R
authors.initials: AR
authors.lastname: Tuite
authors.forename: David N
authors.initials: DN
authors.lastname: Fisman
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Hupert
authors.forename: Rafael
authors.initials: R
authors.lastname: Meza
authors.forename: Bahman
authors.initials: B
authors.lastname: Davoudi
authors.forename: Krista
authors.initials: K
authors.lastname: English
authors.forename: P
authors.initials: P
authors.lastname: van den Driessche
authors.forename: Fred
authors.initials: F
authors.lastname: Brauer
authors.forename: Junling
authors.initials: J
authors.lastname: Ma
authors.forename: Lauren Ancel
authors.initials: LA
authors.lastname: Meyers
authors.forename: Marek
authors.initials: M
authors.lastname: Smieja
authors.forename: Amy
authors.initials: A
authors.lastname: Greer
authors.forename: Danuta M
authors.initials: DM
authors.lastname: Skowronski
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Kwong
authors.forename: Jianhong
authors.initials: J
authors.lastname: Wu
authors.forename: Seyed M
authors.initials: SM
authors.lastname: Moghadas
authors.forename: Daniel
authors.initials: D
authors.lastname: Coombs
authors.forename: Robert C
authors.initials: RC
authors.lastname: Brunham
authors.forename: Babak
authors.initials: B
authors.lastname: Pourbohloul
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001955:British Columbia / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D017589:Immunization Programs / Q000458:organization & administration / Q000592:standards; D007223:Infant; D053118:Influenza A Virus, H1N1 Subtype / Q000187:drug effects / Q000302:isolation & purification*; D007252:Influenza Vaccines / Q000627:therapeutic use*; D007251:Influenza, Human / Q000453:epidemiology* / Q000401:mortality / Q000517:prevention & control* / Q000821:virology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care; D058873:Pandemics; D011159:Population Surveillance; D055815:Young Adult
chemical_list: D007252:Influenza Vaccines