JBM  Vol.3 No.9 , September 2015
A Preliminary Study on Spatial Spread Risk of Epidemics by Analyzing the Urban Subway Mobility Data
Author(s) Bu Zhao1, Shunjiang Ni1,2*, Nuo Yong1,2, Xun Ma1,2, Shifei Shen1,2, Xuewei Ji3
ABSTRACT

The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.

 


Cite this paper
Zhao, B. , Ni, S. , Yong, N. , Ma, X. , Shen, S. , Ji, X. (2015) A Preliminary Study on Spatial Spread Risk of Epidemics by Analyzing the Urban Subway Mobility Data. Journal of Biosciences and Medicines, 3, 15-21. doi: 10.4236/jbm.2015.39003.
References
[1]   Grassly, N.C. and Fraser, C. (2008) Mathematical Models of Infectious Disease Transmission. Nature Reviews Microbiology, 6, 477-487. http://dx.doi.org/10.1038/nrmicro1845

[2]   Anderson, R.M. and Roy, R.M. (1991) Infectious Diseases of Humans. Oxford University Press, Oxford.

[3]   Watts, D.J. and Strogatz, S.H. (1998) Collective Dynamics of “Small-World” Networks. Nature, 393, 440-442. http://dx.doi.org/10.1038/30918

[4]   Moore, C. and Newman, M.E.J. (2000) Epidemics and Percolation in Small-World Networks. Physical Review E, 61, 5678-5682. http://dx.doi.org/10.1103/PhysRevE.61.5678

[5]   Kleczkowski, A. and Grenfel, B.T. (1999) Mean-Field-Type Equations for Spread of Epidemics: The “Small World” Model. Physica A, 274, 355-360. http://dx.doi.org/10.1016/S0378-4371(99)00393-3

[6]   Pastor-Satorras, R. and Vespignani, A. (2001) Epidemic Spreading in Scale-free Networks. Physical Review Letters, 86, 3200-3203. http://dx.doi.org/10.1103/PhysRevLett.86.3200

[7]   Pastor-Satorras, R. and Vespignani, A. (2001) Epidemic Dynamics and Endemic States in Complex Networks. Physical Review E, 63, 1-9. http://dx.doi.org/10.1103/physreve.63.066117

[8]   Xia, C.Y., Liu, Z.X., Chen, Z.Q. and Yuan, Z.Z. (2009) Spreading Behavior of SIS Model with Non-Uniform Transmission on Scale-Free Networks. The Journal of China Universities of Posts and Telecommunications, 16, 27-31. http://dx.doi.org/10.1016/S1005-8885(08)60173-9

[9]   Newman, M.E.J. (2002) The Spread of Epidemic Disease on Networks. Physical Review E, 66, 1-12. http://dx.doi.org/10.1103/physreve.66.016128

[10]   Abramson, G. and Kuperman, M. (2001) Small World Effect in an Epidemiological Model. Physical Review Letters, 86, 1-4.

[11]   Moreno, Y., Gomez, J.B. and Pacheco, A.F. (2003) Epidemic Incidence in Correlated Complex Networks. Physical Review E, 68, 1-4. http://dx.doi.org/10.1103/physreve.68.035103

[12]   Grais, R.F., Ellis, J.H. and Glass, G.E. (2003) Assessing the Impact of Airline Travel on the Geographic Spread of Pandemic. European Journal of Epidemiology, 18, 1065-1072.

 
 
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