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

In this paper, the authors develop a method of detecting correlations between epidemic patterns in different regions that are due to human movement and introduce a null model in which the travel-induced correlations are cancelled. They apply this method to the well-documented cases of seasonal influenza outbreaks in the United States and France. In the United States (using data for 1972–2002), the authors observed strong short-range correlations between several states and their immediate neighbors, as well as robust long-range spreading pat-terns resulting from large domestic air-traffic flows. The stability of these results over time allowed the authors to draw conclusions about the possible impact of travel restrictions on epidemic spread. The authors also applied this method to the case of France (1984–2004) and found that on the regional scale, there was no transportation mode that clearly dominated disease spread. The simplicity and robustness of this method suggest that it could be a useful tool for detecting transmission channels in the spread of epidemics. data interpretation, statistical; disease outbreaks; disease transmission; epidemiologic methods; influenza, human; spatial behavior Understanding quantitatively how a disease spreads in modern society is a crucial issue. In particular, the high probability of occurrence of the next influenza pandemi