title: U.S. airport entry screening in response to pandemic influenza: modeling and analysis.
abstract: BACKGROUND
  A stochastic discrete event simulation model was developed to assess the effectiveness of passenger screening for Pandemic Influenza (PI) at U.S. airport foreign entry.
  
  
  METHODS
  International passengers arriving at 18 U.S. airports from Asia, Europe, South America, and Canada were assigned to one of three states: not infected, infected with PI, infected with other respiratory illness. Passengers passed through layered screening then exited the model. 80% screening effectiveness was assumed for symptomatic passengers; 6% asymptomatic passengers.
  
  
  RESULTS
  In the first 100 days of a global pandemic, U.S. airport screening would evaluate over 17 M passengers with 800 K secondary screenings. 11,570 PI infected passengers (majority asymptomatic) would enter the U.S. undetected from all 18 airports. Foreign airport departure screening significantly decreased the false negative (infected/undetected) passengers. U.S. attack rates: no screening (26.9%-30.9%); screening (26.4%-30.6%); however airport screening results in 800 K-1.8 M less U.S. PI cases; 16 K-35 K less deaths (2% fatality rate). Antiviral medications for travel contact prophylaxis (10 contacts/PI passenger) were high - 8.8M. False positives from all 18 airports: 100-200/day.
  
  
  CONCLUSIONS
  Foreign shore exit screening greatly reduces numbers of PI infected passengers. U.S. airport screening identifies 50% infected individuals; efficacy is limited by the asymptomatic PI infected. Screening will not significantly delay arrival of PI via international air transport, but will reduce the rate of new US cases and subsequent deaths.
authors.affiliation: Center for Disaster and Humanitarian Assistance Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814-4799, USA. jdmalone@cdham.org
authors.forename: John D
authors.initials: JD
authors.lastname: Malone
authors.forename: Robert
authors.initials: R
authors.lastname: Brigantic
authors.forename: George A
authors.initials: GA
authors.lastname: Muller
authors.forename: Ashok
authors.initials: A
authors.lastname: Gadgil
authors.forename: Woody
authors.initials: W
authors.lastname: Delp
authors.forename: Benjamin H
authors.initials: BH
authors.lastname: McMahon
authors.forename: Russell
authors.initials: R
authors.lastname: Lee
authors.forename: Jim
authors.initials: J
authors.lastname: Kulesz
authors.forename: F Matthew
authors.initials: FM
authors.lastname: Mihelic
mesh_terms: D000401:Aircraft; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control*; D005060:Europe; D006801:Humans; D007251:Influenza, Human / Q000517:prevention & control*; D007391:International Cooperation; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods; D013269:Stochastic Processes; D014195:Travel; D014481:United States