title: Reactive strategies for containing developing outbreaks of pandemic influenza.
abstract: BACKGROUND
  In 2009 and the early part of 2010, the northern hemisphere had to cope with the first waves of the new influenza A (H1N1) pandemic. Despite high-profile vaccination campaigns in many countries, delays in administration of vaccination programs were common, and high vaccination coverage levels were not achieved. This experience suggests the need to explore the epidemiological and economic effectiveness of additional, reactive strategies for combating pandemic influenza.
  
  
  METHODS
  We use a stochastic model of pandemic influenza to investigate realistic strategies that can be used in reaction to developing outbreaks. The model is calibrated to documented illness attack rates and basic reproductive number (R0) estimates, and constructed to represent a typical mid-sized North American city.
  
  
  RESULTS
  Our model predicts an average illness attack rate of 34.1% in the absence of intervention, with total costs associated with morbidity and mortality of US$81 million for such a city. Attack rates and economic costs can be reduced to 5.4% and US$37 million, respectively, when low-coverage reactive vaccination and limited antiviral use are combined with practical, minimally disruptive social distancing strategies, including short-term, as-needed closure of individual schools, even when vaccine supply-chain-related delays occur. Results improve with increasing vaccination coverage and higher vaccine efficacy.
  
  
  CONCLUSIONS
  Such combination strategies can be substantially more effective than vaccination alone from epidemiological and economic standpoints, and warrant strong consideration by public health authorities when reacting to future outbreaks of pandemic influenza.
authors.affiliation: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
authors.forename: Sigrún
authors.initials: S
authors.lastname: Andradóttir
authors.forename: Wenchi
authors.initials: W
authors.lastname: Chiu
authors.forename: David
authors.initials: D
authors.lastname: Goldsman
authors.forename: Mi Lim
authors.initials: ML
authors.lastname: Lee
authors.forename: Kwok-Leung
authors.initials: KL
authors.lastname: Tsui
authors.forename: Beate
authors.initials: B
authors.lastname: Sander
authors.forename: David N
authors.initials: DN
authors.lastname: Fisman
authors.forename: Azhar
authors.initials: A
authors.lastname: Nizam
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D004196:Disease Outbreaks / Q000517:prevention & control; D005260:Female; D006801:Humans; D017589:Immunization Programs / Q000458:organization & administration / Q000706:statistics & numerical data; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D009864:Ontario / Q000453:epidemiology; D058873:Pandemics / Q000517:prevention & control*; D015995:Prevalence; D011314:Preventive Health Services / Q000379:methods*; D013269:Stochastic Processes; D055815:Young Adult