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The impact of network clustering and assortativity on epidemic behaviour

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  • Badham, Jennifer
  • Stocker, Rob

Abstract

Epidemic models have successfully included many aspects of the complex contact structure apparent in real-world populations. However, it is difficult to accommodate variations in the number of contacts, clustering coefficient and assortativity. Investigations of the relationship between these properties and epidemic behaviour have led to inconsistent conclusions and have not accounted for their interrelationship. In this study, simulation is used to estimate the impact of social network structure on the probability of an SIR (susceptible-infective-removed) epidemic occurring and, if it does, the final size. Increases in assortativity and clustering coefficient are associated with smaller epidemics and the impact is cumulative. Derived values of the basic reproduction ratio (R0) over networks with the highest property values are more than 20% lower than those derived from simulations with zero values of these network properties.

Suggested Citation

  • Badham, Jennifer & Stocker, Rob, 2010. "The impact of network clustering and assortativity on epidemic behaviour," Theoretical Population Biology, Elsevier, vol. 77(1), pages 71-75.
    • Handle: RePEc:eee:thpobi:v:77:y:2010:i:1:p:71-75
    DOI: 10.1016/j.tpb.2009.11.003
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    References listed on IDEAS

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    1. Jennifer Badham & Rob Stocker, 2010. "A Spatial Approach to Network Generation for Three Properties: Degree Distribution, Clustering Coefficient and Degree Assortativity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-11.
    2. Neil M. Ferguson & Matt J. Keeling & W. John Edmunds & Raymond Gani & Bryan T. Grenfell & Roy M. Anderson & Steve Leach, 2003. "Planning for smallpox outbreaks," Nature, Nature, vol. 425(6959), pages 681-685, October.
    3. Badham, Jennifer & Abbass, Hussein & Stocker, Rob, 2008. "Parameterisation of Keeling’s network generation algorithm," Theoretical Population Biology, Elsevier, vol. 74(2), pages 161-166.
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    Cited by:

    1. Sheryl Le Chang & Mahendra Piraveenan & Mikhail Prokopenko, 2019. "The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model," IJERPH, MDPI, vol. 16(14), pages 1-31, July.
    2. Li, Yinwei & Jiang, Guo-Ping & Wu, Meng & Song, Yu-Rong & Wang, Haiyan, 2021. "Undirected Congruence Model: Topological characteristics and epidemic spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    4. Nunner, Hendrik & Buskens, Vincent & Teslya, Alexandra & Kretzschmar, Mirjam, 2022. "Health behavior homophily can mitigate the spread of infectious diseases in small-world networks," Social Science & Medicine, Elsevier, vol. 312(C).
    5. Li, Xun & Cao, Lang, 2016. "Diffusion processes of fragmentary information on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 624-634.
    6. Shams, Bita & Khansari, Mohammad, 2015. "On the impact of epidemic severity on network immunization algorithms," Theoretical Population Biology, Elsevier, vol. 106(C), pages 83-93.
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    8. Duncan, A.J. & Gunn, G.J. & Umstatter, C. & Humphry, R.W., 2014. "Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks," Theoretical Population Biology, Elsevier, vol. 98(C), pages 11-18.
    9. Chen, Jie & Tan, Xuegang & Cao, Jinde & Li, Ming, 2022. "Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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