title: The cost of simplifying air travel when modeling disease spread.
abstract: BACKGROUND
  Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease.
  
  
  RESULTS
  Using U.S. ticket data from 2007, we compared a simplified "pipe" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a "gravity" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model.
  
  
  CONCLUSIONS
  If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.
authors.affiliation: Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Lessler
authors.forename: James H
authors.initials: JH
authors.lastname: Kaufman
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Ford
authors.forename: Judith V
authors.initials: JV
authors.lastname: Douglas
mesh_terms: D000401:Aircraft; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D012680:Sensitivity and Specificity; D014195:Travel / Q000706:statistics & numerical data*; D014481:United States