Spreading Dynamics of a Viral Infection in a Complex Network
Commenced in January 2007
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Spreading Dynamics of a Viral Infection in a Complex Network

Authors: Khemanand Moheeput, Smita S. D. Goorah, Satish K. Ramchurn

Abstract:

We report a computational study of the spreading dynamics of a viral infection in a complex (scale-free) network. The final epidemic size distribution (FESD) was found to be unimodal or bimodal depending on the value of the basic reproductive number R0 . The FESDs occurred on time-scales long enough for intermediate-time epidemic size distributions (IESDs) to be important for control measures. The usefulness of R0 for deciding on the timeliness and intensity of control measures was found to be limited by the multimodal nature of the IESDs and by its inability to inform on the speed at which the infection spreads through the population. A reduction of the transmission probability at the hubs of the scale-free network decreased the occurrence of the larger-sized epidemic events of the multimodal distributions. For effective epidemic control, an early reduction in transmission at the index cell and its neighbors was essential.

Keywords: Basic reproductive number, epidemic control, scalefree network, viral infection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087025

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References:


[1] V. Colizza, M. Barthélemy, A. Barrat and A. Vespignani, “Epidemic modeling in complex realities,” C. R. Biologies vol. 330, pp 364–374, 2007.
[2] A-L. Barabási, R. Albert and H. Jeong, “Mean-field theory for scale-free random networks,” Physica A vol. 272, pp 173-87, 1999.
[3] S. K. Ramchurn,, K. Moheeput and S. S. Goorah, “An analysis of a short-lived outbreak of dengue fever in Mauritius,” Euro. Surveill. vol. 14, pii = 19314, 2009.
[4] Y. Wanga, G. Xiao, J. Hua, T. H. Chenga and L. Wang, “Imperfect targeted immunization in scale-free networks,” Physica A vol. 388, pp. 2535-2546, 2009.
[5] L. Danon, A. P. Ford, T. House, C. P. Jewell, M. J. Keeling, G. O. Roberts, J. V. Ross and M. C. Vernon, “Networks and the epidemiology of infectious disease,” Interdiscip. Perspect. Infect. Dis. 284909 2011.
[6] A. Chang, M. Parrales, J. Jimenez, M. Sobieszczyk, S. Hammer, D. J. Copenhaver and R. P. Kulkarni, “Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries,” Int. J. Health Geogr. vol. 8, 49 2009.
[7] R. Kamadjeu, “Tracking the polio virus down the Congo River: A case study on the use of Google Earth in public health planning and mapping,” Int. J. Health Geogr. vol 8, 4 2009.
[8] S. Lozano-Fuentes, D. Elizondo-Quiroga, J. Farfan-Ale, M. Lorono- Pino, J. Garcia- Rejon, S. Gomez-Carro, et al., “Use of Google Earth to strengthen public health capacity and facilitate management of vectorborne diseases in resource-poor environments.,” Bull. WHO vol. 86, pp. 718–725, 2008.
[9] Z. Dezsı, and A-L Barabási, “Halting viruses in scale-free networks,” Phys. Rev. E vol. 65, 055103(R) 2002.
[10] R. Pastor-Satorras and A. Vespignani, “Immunization of complex networks,” Phys. Rev. E vol. 65, 036104 2002.
[11] F. Nian and X. Wang, “Efficient immunization strategies on complex networks,” J. Theor. Biol. vol. 264, pp. 77–83 2010.
[12] H. Zhang, J. Zhang, C. Zhou, M. Small and B. Wang, “Hub nodes inhibit the outbreak of epidemic under voluntary vaccination.,” New J. Phys. vol. 12, 023015 2010.