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American Journal of Epidemiology Advance Access originally published online on October 15, 2007
American Journal of Epidemiology 2007 166(11):1244-1251; doi:10.1093/aje/kwm266
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

ORIGINAL CONTRIBUTIONS

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

Pascal Crépey1,2 and Marc Barthélemy3,4

1 Unité 707, Institut national de la Santé et de la Recherche médicale, Paris, France
2 Unité mixte de Recherche en Santé 707, Université Pierre et Marie Curie-Paris6, Paris, France
3 Centre d'Etudes de Bruyères-Le-Châtel, DAM Ile-de-France, Commissariat à l'Energie atomique, Bruyères-Le-Châtel, France
4 School of Informatics and Center for Biocomplexity, Indiana University, Bloomington, IN

Correspondence to Pascal Crépey, UMR-S 707, INSERM–Université Pierre et Marie Curie, Faculté de Médecine Pierre et Marie Curie, 27 rue de Chaligny, 75571 Paris Cedex 12, France (e-mail: pascal.crepey{at}u707.jussieu.fr).

Received for publication September 4, 2006. Accepted for publication August 17, 2007.

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 patterns 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


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