title: Simulating school closure strategies to mitigate an influenza epidemic.
abstract: BACKGROUND
  There remains substantial debate over the impact of school closure as a mitigation strategy during an influenza pandemic. The ongoing 2009 H1N1 influenza pandemic has provided an unparalleled opportunity to test interventions with the most up-to-date simulations.
  
  
  METHODS
  To assist the Allegheny County Health Department during the 2009 H1N1 influenza pandemic, the University of Pittsburgh Models of Infectious Disease Agents Study group employed an agent-based computer simulation model (ABM) of Allegheny County, Pennsylvania, to explore the effects of various school closure strategies on mitigating influenza epidemics of different reproductive rates (R0).
  
  
  RESULTS
  Entire school system closures were not more effective than individual school closures. Any type of school closure may need to be maintained throughout most of the epidemic (ie, at least 8 weeks) to have any significant effect on the overall serologic attack rate. In fact, relatively short school closures (ie, 2 weeks or less) may actually slightly increase the overall attack rate by returning susceptible students back into schools in the middle of the epidemic. Varying the illness threshold at which school closures are triggered did not seem to have substantial impact on the effectiveness of school closures, suggesting that short delays in closing schools should not cause concern.
  
  
  CONCLUSIONS
  School closures alone may not be able to quell an epidemic but, when maintained for at least 8 weeks, could delay the epidemic peak for up to a week, providing additional time to implement a second more effective intervention such as vaccination.
authors.affiliation: University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. BYL1@pitt.edu
authors.forename: Bruce Y
authors.initials: BY
authors.lastname: Lee
authors.forename: Shawn T
authors.initials: ST
authors.lastname: Brown
authors.forename: Philip
authors.initials: P
authors.lastname: Cooley
authors.forename: Maggie A
authors.initials: MA
authors.lastname: Potter
authors.forename: William D
authors.initials: WD
authors.lastname: Wheaton
authors.forename: Ronald E
authors.initials: RE
authors.lastname: Voorhees
authors.forename: Samuel
authors.initials: S
authors.lastname: Stebbins
authors.forename: John J
authors.initials: JJ
authors.lastname: Grefenstette
authors.forename: Shanta M
authors.initials: SM
authors.lastname: Zimmer
authors.forename: Richard K
authors.initials: RK
authors.lastname: Zimmerman
authors.forename: Tina-Marie
authors.initials: TM
authors.lastname: Assi
authors.forename: Rachel R
authors.initials: RR
authors.lastname: Bailey
authors.forename: Diane K
authors.initials: DK
authors.lastname: Wagener
authors.forename: Donald S
authors.initials: DS
authors.lastname: Burke
mesh_terms: D000328:Adult; D002138:Calibration / Q000592:standards; D002648:Child; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control; D017598:Efficiency, Organizational; D004781:Environmental Exposure / Q000706:statistics & numerical data; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000472:pathogenicity; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission; D015233:Models, Statistical; D010414:Pennsylvania / Q000453:epidemiology; D011322:Primary Prevention / Q000379:methods*; D011790:Quarantine / Q000379:methods* / Q000706:statistics & numerical data; D012111:Residence Characteristics / Q000145:classification; D012574:Schools / Q000706:statistics & numerical data; D014195:Travel / Q000706:statistics & numerical data