The advantage of an increasingly interconnected physical world has generated a dangerous by-product: the threat of major pandemics such as HIV-AIDS, SARS, or more recently the threat of pandemic influenza. In an attempt to forecast with accuracy the spread and worldwide impact of epidemics, an international team of researchers from CNRS, CEA, the University Paris-Sud and Indiana University (US), has studied the role played by air travel in this process.1
For the first time, scientists were able to access the International Air Transport Association database: This includes information from the largest 3100 airports in the world, 265 airlines (including number of passengers on each flight) and their flight connections (99% of all international air traffic). By matching this information with disease patterns from cities and census information from 220 countries, they were able to achieve significant predictions.
The research team, led by Alessandro Vespignani, CNRS researcher and professor of informatics at the University of Indiana, used a stochastic model (a statistical process in time involving random variables) to obtain evolving maps of contamination levels or to monitor the disease evolution.
Researchers took into account two factors with opposing effects on predictability. On the one hand, a large airport has many connecting flights, which reduces predictability (any passenger can travel to one of 200 possible destinations). “On the other hand, we were able to identify which routes were the most widely used, a factor that increases predictability,” states Alain Barrat, co-author of the study and researcher at the Theoretical Physics laboratory of Orsay.2 Therefore routes through which an epidemic spreads can be identified, and the accuracy of those predictions can be quantified.
This study is extremely useful since it gives government and health agencies a reliable reading of how a global epidemic spreads. “Inclusion of additional data, such as hygienic conditions in various countries or seasonal travel forecasts can make these predictions even more accurate,” concludes Barrat.
1. V. Colizza et al., “The role of the airline transportation network in the prediction and predictability of global epidemics,” PNAS. 103 (7): 2015-20. 2006.
2. Laboratoire de Physique Théorique d'Orsay (CNRS / Université Paris-XI joint lab).
Laboratoire de Physique Théorique, Orsay.