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Detecting Robust Patterns in the Spread of Epidemics: A Case Study of Influenza in the United States and France
</NOBR><NOBR>Pascal Cr?pey<SUP>1</SUP><SUP>,2</SUP></NOBR> and <NOBR>Marc Barth?lemy<SUP>3</SUP><SUP>,4</SUP></NOBR>
<SUP>1</SUP> Unit? 707, Institut national de la Sant? et de la Recherche m?dicale, Paris, France
<SUP>2</SUP> Unit? mixte de Recherche en Sant? 707, Universit? Pierre et Marie Curie-Paris6, Paris, France
<SUP>3</SUP> Centre d'Etudes de Bruy?res-Le-Ch?tel, DAM Ile-de-France, Commissariat ? l'Energie atomique, Bruy?res-Le-Ch?tel, France
<SUP>4</SUP> School of Informatics and Center for Biocomplexity, Indiana University, Bloomington, IN
Correspondence to Pascal Cr?pey, UMR-S 707, INSERM?Universit? Pierre et Marie Curie, Facult? de M?decine Pierre et Marie Curie, 27 rue de Chaligny, 75571 Paris Cedex 12, France (e-mail: pascal.crepey@u707.jussieu.fr<SCRIPT type=text/javascript><!-- var u = "pascal.crepey", d = "u707.jussieu.fr"; document.getElementById("em0").innerHTML = '<a href="mailto:' + u + '@' + d + '">' + u + '@' + d + '<\/a>'//--></SCRIPT> ).
Received for publication September 4, 2006. Accepted for publication August 17, 2007.
<!-- ABS -->In this paper, the authors develop a method of detecting correlations<SUP> </SUP>between epidemic patterns in different regions that are due<SUP> </SUP>to human movement and introduce a null model in which the travel-induced<SUP> </SUP>correlations are cancelled. They apply this method to the well-documented<SUP> </SUP>cases of seasonal influenza outbreaks in the United States and<SUP> </SUP>France. In the United States (using data for 1972?2002),<SUP> </SUP>the authors observed strong short-range correlations between<SUP> </SUP>several states and their immediate neighbors, as well as robust<SUP> </SUP>long-range spreading patterns resulting from large domestic<SUP> </SUP>air-traffic flows. The stability of these results over time<SUP> </SUP>allowed the authors to draw conclusions about the possible impact<SUP> </SUP>of travel restrictions on epidemic spread. The authors also<SUP> </SUP>applied this method to the case of France (1984?2004)<SUP> </SUP>and found that on the regional scale, there was no transportation<SUP> </SUP>mode that clearly dominated disease spread. The simplicity and<SUP> </SUP>robustness of this method suggest that it could be a useful<SUP> </SUP>tool for detecting transmission channels in the spread of epidemics.<SUP> </SUP>
data interpretation; statistical; disease outbreaks; disease transmission; epidemiologic methods; influenza; human; spatial behavior
</NOBR><NOBR>Pascal Cr?pey<SUP>1</SUP><SUP>,2</SUP></NOBR> and <NOBR>Marc Barth?lemy<SUP>3</SUP><SUP>,4</SUP></NOBR>
<SUP>1</SUP> Unit? 707, Institut national de la Sant? et de la Recherche m?dicale, Paris, France
<SUP>2</SUP> Unit? mixte de Recherche en Sant? 707, Universit? Pierre et Marie Curie-Paris6, Paris, France
<SUP>3</SUP> Centre d'Etudes de Bruy?res-Le-Ch?tel, DAM Ile-de-France, Commissariat ? l'Energie atomique, Bruy?res-Le-Ch?tel, France
<SUP>4</SUP> School of Informatics and Center for Biocomplexity, Indiana University, Bloomington, IN
Correspondence to Pascal Cr?pey, UMR-S 707, INSERM?Universit? Pierre et Marie Curie, Facult? de M?decine Pierre et Marie Curie, 27 rue de Chaligny, 75571 Paris Cedex 12, France (e-mail: pascal.crepey@u707.jussieu.fr<SCRIPT type=text/javascript><!-- var u = "pascal.crepey", d = "u707.jussieu.fr"; document.getElementById("em0").innerHTML = '<a href="mailto:' + u + '@' + d + '">' + u + '@' + d + '<\/a>'//--></SCRIPT> ).
Received for publication September 4, 2006. Accepted for publication August 17, 2007.
<!-- ABS -->In this paper, the authors develop a method of detecting correlations<SUP> </SUP>between epidemic patterns in different regions that are due<SUP> </SUP>to human movement and introduce a null model in which the travel-induced<SUP> </SUP>correlations are cancelled. They apply this method to the well-documented<SUP> </SUP>cases of seasonal influenza outbreaks in the United States and<SUP> </SUP>France. In the United States (using data for 1972?2002),<SUP> </SUP>the authors observed strong short-range correlations between<SUP> </SUP>several states and their immediate neighbors, as well as robust<SUP> </SUP>long-range spreading patterns resulting from large domestic<SUP> </SUP>air-traffic flows. The stability of these results over time<SUP> </SUP>allowed the authors to draw conclusions about the possible impact<SUP> </SUP>of travel restrictions on epidemic spread. The authors also<SUP> </SUP>applied this method to the case of France (1984?2004)<SUP> </SUP>and found that on the regional scale, there was no transportation<SUP> </SUP>mode that clearly dominated disease spread. The simplicity and<SUP> </SUP>robustness of this method suggest that it could be a useful<SUP> </SUP>tool for detecting transmission channels in the spread of epidemics.<SUP> </SUP>
data interpretation; statistical; disease outbreaks; disease transmission; epidemiologic methods; influenza; human; spatial behavior
