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A stochastic equation-based model of the value of international air-travel restrictions for controlling pandemic flu
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Source Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents
Washington D.C.
SESSION: Health care: epidemic models table of contents
Pages: 1538-1542  
Year of Publication: 2007
ISBN:1-4244-1306-0
Authors
D. Michael Goedecke  RTI International, Research Triangle Park, NC
Georgiy V. Bobashev  RTI International, Research Triangle Park, NC
Feng Yu  RTI International, Research Triangle Park, NC
Sponsors
INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
Publisher
IEEE Press  Piscataway, NJ, USA
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ABSTRACT

International air travel can be an important contributing factor to the global spread of infectious diseases, as evidenced by the outbreak of Severe Acute Respiratory Syndrome in 2003. Restrictions on air travel may therefore be one response to attempt to control a widespread epidemic of a disease such as influenza. We present results from a stochastic, equation-based, global epidemic model which suggest that air travel restrictions often provide only a slight delay in the epidemic. This delay may give valuable time in which to implement other disease control strategies; however, if other strategies are not implemented, the use of travel restrictions alone may lead to a more severe epidemic than if they had not been imposed. Our results also indicate that the particular network of cities chosen for modeling can have a great influence on the model results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Baroyan, O. V., G. A. Mironov, and L. A. Rvachev. 1981. An algorithm modeling global epidemics of mutant origin. Programming and Computer Software 6(5):272--277. English translation from Programmirovanie 5:73--79. 1980 (in Russian).
 
2
Brinkhoff, T. 2005. Mato Grosso City Population. Available via <http://www.citypopulation.de/Brazil-MatoGrosso.html>
 
3
Colizza, V., A. Barrat, M. Barthélemy, and A. Vespignani. 2006a. The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the U. S. A. 103(7):2015--2020.
 
4
Colizza, V., A. Barrat, M. Barthélemy, and A. Vespignani. 2006b. The modeling of global epidemics: Stochastic dynamics and predictability. Bulletin of Mathematical Biology 68:1893--1921.
 
5
Colizza, V., A. Barrat, M. Barthelemy, A. J. Valleron, and A. Vespignani. 2007. Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Medicine 4(1):e13.
 
6
Cooper, B. S., R. J. Pitman, W. J. Edmunds, and N. J. Gay. 2006. Delaying the international spread of pandemic influenza. PLoS Medicine 3(6):e212.
 
7
Epstein, J. M., D. M. Goedecke, F. Yu, R. J. Morris, D. K. Wagener, and G. V. Bobashev. 2007. Controlling Pandemic Flu: The Value of International Air Travel Restrictions. PLoS ONE 2(5):e401.
 
8
ESRI. 2005. ArcGIS 9 World, Europe, Canada, and Mexico: 1996, 1998, Winter 1993/1994. {Computer software and data files 20000101, 2000, 20000225, 20010128, 20000612, 20020314, 20021115, 2000, 2003}. Redlands, CA: ESRI.
 
9
Eubank, S., H. Guclu, V. S. A. Kumar, M. V. Marathe, A. Srinivasan, Z. Toroczkai, and N. Wang. 2004. Modeling disease outbreaks in realistic urban social networks. Nature 429:180--184.
 
10
Ferguson, N. M., D. A. T. Cummings, S. Cauchemez, C. Fraser, S. Riley, A. Meeyai, S. Iamsirithaworn, and D. S. Burke. 2005. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437:209--214.
 
11
Ferguson, N. M., D. A. T. Cummings, C. Fraser, J. C. Cajka, P. C. Cooley, and D. S. Burke. 2006. Strategies for mitigating an influenza pandemic. Nature 442:448--452.
 
12
Germann, T. C., K. Kadau, I. M. Longini Jr, C. A. Macken. 2006. Mitigation strategies for pandemic influenza in the United States. Proceedings of the National Academy of Sciences of the U. S. A. 103(15):5935--5940.
 
13
Grais, R. F., J. H. Ellis, and G. E. Glass. 2003. Assessing the impact of airline travel on the geographic spread of pandemic influenza. European Journal of Epidemiology. 18:1065--1072.
 
14
Grais, R. F., J. H. Ellis, A. Kress, and G. E. Glass. 2004. Modeling the spread of annual influenza epidemics in the U.S.: The potential role of air travel. Health Care Management Science 7:127--134.
 
15
Guimerà, R., S. Mossa, A. Turtschi, L. A. N. Amaral. 2005. The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles. Proceedings of the National Academy of Sciences of the U. S. A. 102(22):7794--7799.
 
16
Helders, S. 2005. World Gazetteer. Available via <http://www.world-gazetteer.com> {accessed April 20, 2006}.
 
17
Hufnagel, L., D. Brockmann, and T. Geisel. 2004. Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the U. S. A. 101(42):15124--15129.
 
18
Instituto Brasileiro de Geografia e Estatística (IBGE). 2006. Available via <http://www.ibge.gov.br> {accessed April 20, 2006}.
 
19
Longini, Jr., I. M., A. Nizam, S. Xu, K. Ungchusak, W. Hanshaoworakul, D. A. T. Cummings, and M. E. Halloran. 2005. Containing pandemic influenza at the source. Science 309:1083--1087.
 
20
Mongabay.com. 2004. World Population Figures. Available via <http://population.mongabay.com> {accessed April 24, 2006}.
 
21
Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects. 2004. World Urbanization Prospects: The 2003 Revision Population Database. Available via <http://esa.un.org/unup> {accessed April, 2006}.
 
22
Population Division, U. S. Census Bureau. 2004. Table 1. Annual Estimates of the Population of Metropolitan and Micropolitan Statistical Areas: April 1, 2000 to July 1, 2004 (CBSA-EST2004-01). Available via <http://www.census.gov/population/www/estimates/Estimates%20pages_final.html> {accessed April, 2006}.
 
23
Rvachev, L. A., I. M. Longini, Jr. 1985. A mathematical model for the global spread of influenza. Mathematical Biosciences 75:3--22.
Collaborative Colleagues:
D. Michael Goedecke: colleagues
Georgiy V. Bobashev: colleagues
Feng Yu: colleagues