Open Access Highly Accessed Research article

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

Juliette Stehlé1*, Nicolas Voirin2,3, Alain Barrat1,4, Ciro Cattuto4, Vittoria Colizza5,6,7, Lorenzo Isella4, Corinne Régis3, Jean-François Pinton8, Nagham Khanafer2,3, Wouter Van den Broeck4 and Philippe Vanhems2,3

1 Centre de Physique Théorique de Marseille, CNRS UMR 6207, Marseille, France

2 Hospices Civils de Lyon, Hôpital Edouard Herriot, Service d'Hygiène, Epidémiologie et Prévention, Lyon, France

3 Université de Lyon; université Lyon 1; CNRS UMR 5558, laboratoire de Biométrie et de Biologie Evolutive, Equipe Epidémiologie et Santé Publique, Lyon, France

4 Data Science Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy

5 INSERM, U707, Paris F-75012, France

6 UPMC Université Paris 06, Faculté de Médecine Pierre et Marie Curie, UMR S 707, Paris F75012, France

7 Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy

8 Laboratoire de Physique de l'Ecole Normale Supérieure de Lyon, CNRS UMR 5672, Lyon, France

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BMC Medicine 2011, 9:87 doi:10.1186/1741-7015-9-87

Published: 19 July 2011

Additional files

Additional file 1:

Supporting text. Description of the data-extension procedure CONSTR-SH (constrained shuffling_.

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Additional file 2:

Supplementary figures 1-3. Snapshots of the contact graph between the 405 attendees for the first conference day.

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Additional file 3:

Supplementary table 1. Mean values, variances and 90% CI of R0 according to the different scenarios and network types.

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Additional file 4:

Supplementary figure 4. Box plots showing the distributions of the number of final cases when the final attack rate is larger than 10%, according to the different scenarios and network types.

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Additional file 5:

Supplementary table 2. Mean values, variances and 90% CI of the prevalence peak time tpeak according to the different scenarios and network types.

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