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In this paper, the authors develop a method of detecting correlations betweenepidemic patterns in different regions that are due to human movement andintroduce a null model in which the travel-induced correlations are cancelled.They apply this method to the well-documented cases of seasonal influenzaoutbreaks in the United States and France. In the United States (using data for1972-2002), the authors observed strong short-range correlations betweenseveral states and their immediate neighbors, as well as robust long-rangespreading patterns resulting from large domestic air-traffic flows. Thestability of these results over time allowed the authors to draw conclusionsabout the possible impact of travel restrictions on epidemic spread. Theauthors also applied this method to the case of France (1984-2004) and foundthat on the regional scale, there was no transportation mode that clearlydominated disease spread. The simplicity and robustness of this method suggestthat it could be a useful tool for detecting transmission channels in thespread of epidemics.

Detecting Robust Patterns in the Spread of Epidemics: A Case Study of Influenza in the United States and France