Quantitatively evaluating interventions in the influenza A (H1N1) epidemic on China campus grounded on individual-based simulations

The novel Influenza A (H1N1) virus is attacking the world in 2009. Among others, campuses in China, particularly most university/college campuses for bachelor students, are at-risk areas where many susceptible youngsters live. They most likely interact with each other quite often in dormitories, classrooms and refectories. We model the pandemic influenza A (H1N1) transmission through campus contacts and then forecast the effectiveness of interventions, based on a previously presented Complex Agent Network model for simulating infectious diseases [1]. Our results suggest that pandemic influenza A (H1N1) on campus will die out even with no intervention taken; the most effective intervention is still quarantining confirmed cases as early as possible and, in addition, vaccinating susceptible people can further decrease the maximum daily number of the infected. This study can support quantitative experimentation and prediction of infectious diseases within predefined areas, and assessment of intervention strategies.

[1]  David N. Durrheim,et al.  Influenza: H1N1 goes to school. , 2009, Science.

[2]  Bjoern Peters,et al.  Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population , 2009, Proceedings of the National Academy of Sciences.

[3]  Peter J. Huber,et al.  Massive Data Sets , 2011 .

[4]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[5]  Derek Gatherer,et al.  The 2009 H1N1 influenza outbreak in its historical context. , 2009, Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology.

[6]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[7]  Catherine H Mercer,et al.  Scale-Free Networks and Sexually Transmitted Diseases: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe , 2004, Sexually transmitted diseases.

[8]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Wang Wei-ping Application of Autonomous Agent Modeling in Naval Tactical Simulation , 2008 .

[10]  Shan Meia,et al.  UvA-DARE ( Digital Academic Repository ) Quantitatively evaluating interventions in the influenza A ( H 1 N 1 ) epidemic on China campus grounded on individual-based simulations , 2010 .

[11]  Joyce Tait,et al.  The economy-wide impact of pandemic influenza on the UK: a computable general equilibrium modelling experiment , 2009, BMJ : British Medical Journal.

[12]  Dominic A Fitzgerald,et al.  Human swine influenza A [H1N1]: practical advice for clinicians early in the pandemic. , 2009, Paediatric respiratory reviews.

[13]  Joseph N S Eisenberg,et al.  Protecting the herd from H1N1. , 2009, Science.

[14]  Fan Chung Graham,et al.  Random evolution in massive graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[15]  Fan Chung Graham,et al.  A random graph model for massive graphs , 2000, STOC '00.

[16]  Yifan Zhu,et al.  Complex agent networks explaining the HIV epidemic among homosexual men in Amsterdam , 2008, Math. Comput. Simul..

[17]  K. Choi,et al.  Widespread public misconception in the early phase of the H1N1 influenza epidemic. , 2009, The Journal of infection.

[18]  J. Medlock,et al.  Optimizing Influenza Vaccine Distribution , 2009, Science.

[19]  Gail E. Potter,et al.  The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus , 2009, Science.

[20]  L. Amaral,et al.  The web of human sexual contacts , 2001, Nature.

[21]  Marion Koopmans,et al.  Pathogenesis and Transmission of Swine-Origin 2009 A(H1N1) Influenza Virus in Ferrets , 2009, Science.

[22]  D. Owens,et al.  Effectiveness and cost-effectiveness of vaccination against pandemic influenza (H1N1) 2009. , 2009 .

[23]  Luís A. Nunes Amaral,et al.  Sexual networks: implications for the transmission of sexually transmitted infections. , 2003, Microbes and infection.

[24]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[25]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.