title: FLIRT-ing with Zika: A Web Application to Predict the Movement of Infected Travelers Validated Against the Current Zika Virus Epidemic.
abstract: BACKGROUND
  Beginning in 2015, Zika virus rapidly spread throughout the Americas and has been linked to neurological and autoimmune diseases in adults and babies. Developing accurate tools to anticipate Zika spread is one of the first steps to mitigate further spread of the disease. When combined, air traffic data and network simulations can be used to create tools to predict where infectious disease may spread to and aid in the prevention of infectious diseases. Specific goals were to: 1) predict where travelers infected with the Zika Virus would arrive in the U.S.; and, 2) analyze and validate the open access web application's (i.e., FLIRT) predictions using data collected after the prediction was made.
  
  
  METHODS
  FLIRT was built to predict the flow and likely destinations of infected travelers through the air travel network. FLIRT uses a database of flight schedules from over 800 airlines, and can display direct flight traffic and perform passenger simulations between selected airports. FLIRT was used to analyze flights departing from five selected airports in locations where sustained Zika Virus transmission was occurring. FLIRT's predictions were validated against Zika cases arriving in the U.S. from selected airports during the selected time periods.  Kendall's τ and Generalized Linear Models were computed for all permutations of FLIRT and case data to test the accuracy of FLIRT's predictions.
  
  
  RESULTS
  FLIRT was found to be predictive of the final destinations of infected travelers in the U.S. from areas with ongoing transmission of Zika in the Americas from 01 February 2016 - 01 to April 2016, and 11 January 2016 to 11 March 2016 time periods. MIA-FLL, JFK-EWR-LGA, and IAH were top ranked at-risk metro areas, and Florida, Texas and New York were top ranked states at-risk for the future time period analyzed (11 March 2016 - 11 June 2016). For the 11 January 2016 to 11 March 2016 time period, the region-aggregated model indicated 7.24 (95% CI 6.85 - 7.62) imported Zika cases per 100,000 passengers, and the state-aggregated model suggested 11.33 (95% CI 10.80 - 11.90) imported Zika cases per 100,000 passengers.
  
  
  CONCLUSIONS
  The results from 01 February 2016 to 01 April 2016 and 11 January 2016 to 11 March 2016 time periods support that modeling air travel and passenger movement can be a powerful tool in predicting where infectious diseases will spread next. As FLIRT was shown to significantly predict distribution of Zika Virus cases in the past, there should be heightened biosurveillance and educational campaigns to medical service providers and the general public in these states, especially in the large metropolitan areas.
authors.forename: Andrew
authors.initials: A
authors.lastname: Huff
authors.affiliation: Technology & Data Science, EcoHealth Alliance, New York, NY, USA.
authors.forename: Toph
authors.initials: T
authors.lastname: Allen
authors.affiliation: Technology & Data Science, EcoHealth Alliance, New York, NY, USA.
authors.forename: Karissa
authors.initials: K
authors.lastname: Whiting
authors.affiliation: Technology & Data Science, EcoHealth Alliance, New York, NY, USA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Breit
authors.affiliation: Technology & Data Science, EcoHealth Alliance, New York, NY, USA.
authors.forename: Brock
authors.initials: B
authors.lastname: Arnold
keywords: Zika; air travel; disease forecasting; flirt; networks; zika virus