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Danon, Leon, Ford, Ashley P., House, Thomas A., Jewell, Chris P., Keeling, Matthew James, Roberts, Gareth O., Ross, Joshua V. and Vernon, Matthew C.. (2011) Networks and the epidemiology of infectious disease. Interdisciplinary Perspectives on Infectious Diseases, Vol.2011 . Article no. 284909. ISSN 1687708X

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Official URL: http://dx.doi.org/10.1155/2011/284909
Abstract
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the everexpanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.
[error in script] [error in script]Item Type:  Journal Article 

Subjects:  Q Science > QA Mathematics R Medicine > RA Public aspects of medicine 
Divisions:  Faculty of Science > Life Sciences (2010 ) Faculty of Science > Mathematics Faculty of Science > Statistics 
Library of Congress Subject Headings (LCSH):  System analysis, Epidemiology  Mathematical models, Communicable diseases  Mathematical models 
Journal or Publication Title:  Interdisciplinary Perspectives on Infectious Diseases 
Publisher:  Hindawi Publishing Corporation 
ISSN:  1687708X 
Date:  2011 
Volume:  Vol.2011 
Number of Pages:  28 
Page Range:  Article no. 284909 
Identification Number:  10.1155/2011/284909 
Status:  Peer Reviewed 
Publication Status:  Published 
Access rights to Published version:  Open Access 
Funder:  Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Medical Research Council (Great Britain) (MRC), United States. Dept. of Homeland Security. Science and Technology Directorate, Australian Research Council (ARC) 
Grant number:  DP110102893 (ARC) 
References:  [1] A. S. Klovdahl, “Social networks and the spread of infectious diseases: the AIDS example,” Social Science andMedicine, vol. 21, no. 11, pp. 1203–1216, 1985. [2] R. M. May and R. M. Anderson, “Transmission dynamics of HIV infection,” Nature, vol. 326, no. 6109, pp. 137–142, 1987. [3] D. C. Bell, J. S. Atkinson, and J.W. Carlson, “Centrality measures for disease transmission networks,” Social Networks, vol. 21, no. 1, pp. 1–21, 1999. [4] H. L. Goodman, “Snowball sampling,” Annals of Mathematical Statistics, vol. 32, pp. 148–170, 1961. [5] D. D. Heckathorn, “Respondentdriven sampling: a new approach to the study of hidden populations,” Social Problems, vol. 44, no. 2, pp. 174–199, 1997. [6] A. M. Jolly and J. L. Wylie, “Sampling individuals with large sexual networks: an evaluation of four approaches,” Sexually Transmitted Diseases, vol. 28, no. 4, pp. 200–207, 2001. [7] J. L.Wylie and A. Jolly, “Patterns of chlamydia and gonorrhea infection in sexual networks in Manitoba, Canada,” Sexually Transmitted Diseases, vol. 28, no. 1, pp. 14–24, 2001. [8] A. S. Klovdahl, J. J. Potterat, D. E. Woodhouse, J. B.Muth, S. Q.Muth, andW.W. Darrow, “Social networks and infectious disease: the Colorado Springs Study,” Social Science and Medicine, vol. 38, no. 1, pp. 79–88, 1994. [9] A. C. Ghani, C. A. Donnelly, and G. P. Garnett, “Sampling biases and missing data in explorations of sexual partner networks for the spread of sexually transmitted diseases,” Statistics in Medicine, vol. 17, no. 18, pp. 2079–2097, 1998. [10] R. M. Anderson and R. M. May, Infectious Diseases of Humans, Oxford University Press, Oxford, UK, 1992. [11] N. Eagle and A. Pentland, “Reality mining: sensing complex social systems,” Personal and Ubiquitous Computing, vol. 10, no. 4, pp. 255–268, 2006. [12] R. K. Hamede, J. Bashford, H. McCallum, and M. Jones, “Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease,” Ecology Letters, vol. 12, no. 11, pp. 1147–1157, 2009. [13] A. M. Johnson, J. Wadsworth, K. Wellings, S. Bradshaw, and J. Field, “Sexual lifestyles and HIV risk,” Nature, vol. 360, no. 6403, pp. 410–412, 1992. [14] A. Johnson, J. Wadsworth, K. Wellings, and J. Field, Sexual Attitudes and Lifestyles, Blackwell Scientific Publications, Oxford, UK, 1994. [15] A. M. Johnson, C. H. Mercer, B. Erens et al., “Sexual behaviour in Britain: partnerships, practices, and HIV risk behaviours,” Lancet, vol. 358, no. 9296, pp. 1835–1842, 2001. [16] A. J. Copas, K. Wellings, B. Erens et al., “The accuracy of reported sensitive sexual behaviour in Britain: exploring the extent of change 1990–2000,” Sexually Transmitted Infections, vol. 78, no. 1, pp. 26–30, 2002. [17] J.Mossong, N. Hens, M. Jit et al., “Social contacts and mixing patterns relevant to the spread of infectious diseases,” PLoS Medicine, vol. 5, no. 3, article e74, pp. 381–391, 2008. [18] M.Molloy and B. Reed, “A criticalpoint for randomgraphswith a given degree sequence,” Random Structures and Algorithms, vol. 6, pp. 161–179, 1995. [19] M.Molloy and B. Reed, “The size of the giant component of a random graph with a given degree sequence,” Combinatorics Probability and Computing, vol. 7, no. 3, pp. 295–305, 1998. [20] J. M. Read, K. T.D. Eames, and W. J. Edmunds, “Dynamic social networks and the implications for the spread of infectious disease,” Journal of the Royal Society Interface, vol. 5, no. 26, pp. 1001–1007, 2008. [21] M. Baguelin, A. J. V. Hoek, M. Jit, S. Flasche, P. J. White, andW. J. Edmunds, “Vaccination against pandemic influenza A/H1N1v in England: a realtime economic evaluation,” Vaccine, vol. 28, no. 12, pp. 2370–2384, 2010. [22] F. Liljeros, C. R. Edling, L. Amaral, H. E. Stanley, and Y. A° berg, “The web of human sexual contacts,” Nature, vol. 411, no. 6840, pp. 907–908, 2001. [23] A. C. Ghani and G. P. Garnett, “Risks of acquiring and transmitting sexually transmitted diseases in sexual partner networks,” Sexually Transmitted Diseases, vol. 27, no. 10, pp. 579–587, 2000. [24] J. Medlock and A. P. Galvani, “Optimizing influenza vaccine distribution,” Science, vol. 325, no. 5948, pp. 1705–1708, 2009. [25] M. Kretzschmar and M.Morris, “Measures of concurrency in networks and the spread of infectious disease,” Mathematical Biosciences, vol. 133, no. 2, pp. 165–195, 1996. [26] M. Morris and M. Kretzschmar, “Concurrent partnerships and the spread of HIV,” AIDS, vol. 11, no. 5, pp. 641–648, 1997. [27] A. C. Ghani, J. Swinton, and G. P. Garnett, “The role of sexual partnership networks in the epidemiology of gonorrhea,” Sexually Transmitted Diseases, vol. 24, no. 1, pp. 45–56, 1997. [28] M. E. Halloran, I. M. Longini, A. Nizam, and Y. Yang, “Containing bioterrorist smallpox,” Science, vol. 298, no. 5597, pp. 1428–1432, 2002. [29] I. M. Longini, A. Nizam, S. Xu et al., “Containing pandemic influenza at the source,” Science, vol. 309, no. 5737, pp. 1083– 1087, 2005. [30] T. C. Germann, K. Kadau, I. M. Longini, and C. A. Macken, “Mitigation strategies for pandemic influenza in the United States,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 15, pp. 5935–5940, 2006. [31] I. M. Longini Jr., M. E.Halloran, A. Nizam et al., “Containing a large bioterrorist smallpox attack: a computer simulation approach,” International Journal of Infectious Diseases, vol. 11, no. 2, pp. 98–108, 2007. [32] N. M. Ferguson, D. A. T. Cummings, S. Cauchemez et al., “Strategies for containing an emerging influenza pandemic in Southeast Asia,” Nature, vol. 437, no. 7056, pp. 209–214, 2005. [33] N. M. Ferguson, D. A. T. Cummings, C. Fraser, J. C. Cajka, P. C. Cooley, and D. S. Burke, “Strategies for mitigating an influenza pandemic,” Nature, vol. 442, no. 7101, pp. 448–452, 2006. [34] G. Chowell, J. M. Hyman, S. Eubank, and C. CastilloChavez, “Scaling laws for the movement of people between locations in a large city,” Physical Review E, vol. 68, no. 6, Article ID 066102, pp. 661021–661027, 2003. [35] S. Eubank,H. Guclu, V. S. A. Kumar et al., “Modelling disease outbreaks in realistic urban social networks,” Nature, vol. 429, no. 6988, pp. 180–184, 2004. [36] D. L. Chao, M. E. Halloran, V. J. Obenchain, and I. M. Longini, “FluTE, a publicly available stochastic influenza epidemic simulation model,” PLoS Computational Biology, vol. 6, no. 1, article e1000656, 2010. [37] L. Hufnagel, D. Brockmann, and T. Geisel, “Forecast and control of epidemics in a globalized world,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 42, pp. 15124–15129, 2004. [38] R. Guimer`a, S.Mossa, A. Turtschi, and L. A. N. Amaral, “The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 22, pp. 7794–7799, 2005. [39] I. M. Hall, J. R. Egan, I. Barrass, R. Gani, and S. Leach, “Comparison of smallpox outbreak control strategies using a spatial metapopulationmodel,” Epidemiology and Infection, vol. 135, no. 7, pp. 1133–1144, 2007. [40] C. Viboud, O. N. Bjørnstad, D. L. Smith, L. Simonsen, M. A. Miller, and B. T. Grenfell, “Synchrony, waves, and spatial hierarchies in the spread of influenza,” Science, vol. 312, no. 5772, pp. 447–451, 2006. [41] D. Brockmann, L. Hufnagel, and T. Geisel, “The scaling laws of human travel,” Nature, vol. 439, no. 7075, pp. 462–465, 2006. [42] S. E. Robinson, M. G. Everett, and R. M. Christley, “Recent network evolution increases the potential for large epidemics in the British cattle population,” Journal of the Royal Society Interface, vol. 4, no. 15, pp. 669–674, 2007. [43] I. Hanski and O. Gaggiotti, Ecology, Genetics, and Evolution of Metapopulations, Elsevier, New York, NY, USA, 2004. [44] L. Sattenspiel and D. A. Herring, “Structured epidemic models and the spread of influenza in the central Canadian Subarctic,” Human Biology, vol. 70, no. 1, pp. 91–115, 1998. [45] V. Colizza, A. Barrat, M. Barth´elemy, and A. Vespignani, “The role of the airline transportation network in the prediction and predictability of global epidemics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 7, pp. 2015–2020, 2006. [46] V. Colizza, A. Barrat, M. Barthelemy, A. J. Valleron, and A. Vespignani, “Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions,” PLoS Medicine, vol. 4, no. 1, article 13, pp. 0095–0110, 2007. [47] K. Khan, W. Hu, P. Raposo et al., “Spread of a novel influenza A (H1N1) virus via global airline transportation,” New England Journal ofMedicine, vol. 361, no. 2, pp. 212–214, 2009. [48] D. Balcan, H. Hu, B. Goncalves et al., “Seasonal transmission potential and activity peaks of the new influenza A(H1N1): aMonte Carlo likelihood analysis based on human mobility,” BMC Medicine, vol. 7, article 45, 2009. [49] C. Viboud, M. A. Miller, B. T. Grenfell, O. N. Bjørnstad, and L. Simonsen, “Air travel and the spread of influenza: important caveats,” PLoS Medicine, vol. 3, no. 11, pp. 2159– 2160, 2006. [50] D. Balcan, V. Colizza, B. Gonc¸alves, H. Hud, J. J. Ramasco, and A. Vespignani, “Multiscale mobility networks and the spatial spreading of infectious diseases,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 51, pp. 21484–21489, 2009. [51] L. Danon, T. House, and M. J. Keeling, “The role of routine versus random movements on the spread of disease in Great Britain,” Epidemics, vol. 1, no. 4, pp. 250–258, 2009. [52] M. J. Keeling, L. Danon, M. C. Vernon, and T. A. House, “Individual identity and movement networks for disease metapopulations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 19, pp. 8866–8870, 2010. [53] D. M. Green, I. Z. Kiss, and R. R. Kao, “Modelling the initial spread of footandmouth disease through animal movements,” Proceedings of the Royal Society B, vol. 273, no. 1602, pp. 2729–2735, 2006. [54] M. F. Heath, M. C. Vernon, and C. R. Webb, “Construction of networks with intrinsic temporal structure from UK cattle movement data,” BMC Veterinary Research, vol. 4, article 11, 2008. [55] M. C. Vernon andM. J. Keeling, “Representing theUK’s cattle herd as static and dynamic networks,” Proceedings of the Royal Society B, vol. 276, pp. 469–476, 2009. [56] E. BrooksPollock and M. Keeling, “Herd size and bovine tuberculosis persistence in cattle farms in Great Britain,” Preventive Veterinary Medicine, vol. 92, no. 4, pp. 360–365, 2009. [57] I. Z. Kiss, D. M. Green, and R. R. Kao, “The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread,” Journal of the Royal Society Interface, vol. 3, no. 10, pp. 669–677, 2006. [58] J. C. Gibbens, C. E. Sharpe, J. W. Wilesmith et al., “Descriptive epidemiology of the 2001 footandmouth disease epidemic in Great Britain: the first five months,” Veterinary Record, vol. 149, no. 24, pp. 729–743, 2001. [59] F. Natale, A. Giovannini, L. Savini et al., “Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread,” Preventive VeterinaryMedicine, vol. 92, no. 4, pp. 341–350, 2009. [60] D. T. Haydon, M. ChaseTopping, D. J. Shaw et al., “The construction and analysis of epidemic trees with reference to the 2001 UK footandmouth outbreak,” Proceedings of the Royal Society B, vol. 270, no. 1511, pp. 121–127, 2003. [61] S. Riley, C. Fraser, C. A. Donnelly et al., “Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions,” Science, vol. 300, no. 5627, pp. 1961–1966, 2003. [62] E. M. Cottam, J.Wadsworth, A. E. Shaw et al., “Transmission pathways of footandmouth disease virus in the United Kingdom in 2007,” PLoS Pathogens, vol. 4, no. 4, Article ID e1000050, 2008. [63] M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003. [64] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang, “Complex networks: structure and dynamics,” Physics Reports, 2006. [65] M. Newman, A. Barabasi, and D. Watts, The Structure and Dynamics of Networks, Princeton University Press, Princeton, NJ, USA, 2006. [66] D. M. Boyd and N. B. Ellison, “Social network sites: definition, history, and scholarship,” Journal of Computer Mediated Communication, vol. 13, no. 1, article 11, pp. 210– 230, 2007. [67] M. E. Fisher and J. Esam, “Some cluster size and percolation problems,” Journal of Mathematical Physics, vol. 2, pp. 609– 619, 1961. [68] B. Nickel and D. Wilkinson, “Invasion percolation on the Cayley tree: exact solution of a modified percolation model,” Physical Review Letters, vol. 51, no. 2, pp. 71–74, 1983. [69] F. Ball and P.Neal, “Network epidemic models with two levels of mixing,” Mathematical Biosciences, vol. 212, no. 1, pp. 69– 87, 2008. [70] T. House and M. J. Keeling, “Deterministic epidemic models with explicit household structure,” Mathematical Biosciences, vol. 213, no. 1, pp. 29–39, 2008. [71] D. J. Watts, R. Muhamad, D. C. Medina, and P. S. Dodds, “Multiscale, resurgent epidemics in a hierarchical metapopulation model,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 32, pp. 11157– 11162, 2005. [72] J. L. Lebowitz, C. Maes, and E. R. Speer, “Statistical mechanics of probabilistic cellular automata,” Journal of Statistical Physics, vol. 59, no. 12, pp. 117–170, 1990. [73] C. J. Rhodes and R. M. Anderson, “Epidemic thresholds and vaccination in a lattice model of disease spread,” Theoretical Population Biology, vol. 52, no. 2, pp. 101–118, 1997. [74] D. J.Watts and S.H. Strogatz, “Collective dynamics of “smallworld” networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998. [75] J. Travers and S. Milgram, “An experimental study of the small world problem,” Sociometry, vol. 32, no. 4, pp. 425–443, 1969. [76] M. Boots and A. Sasaki, ““Small worlds” and the evolution of virulence: infection occurs locally and at a distance,” Proceedings of the Royal Society B, vol. 266, no. 1432, pp. 1933–1938, 1999. [77] M. Boots, P. J. Hudson, and A. Sasaki, “Large shifts in pathogen virulence relate to host population structure,” Science, vol. 303, no. 5659, pp. 842–844, 2004. [78] J. Badham, H. Abbass, and R. Stocker, “Parameterisation of Keeling’s network generation algorithm,” Theoretical Population Biology, vol. 74, no. 2, pp. 161–166, 2008. [79] P. W. Holland and S. Leinhardt, “An exponential family of probability distributions for directed graphs,” Journal of the American Statistical Association, vol. 76, no. 373, pp. 33–50, 1981. [80] J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” Journal of the Royal Statistical Society. Series B, vol. 36, no. 2, pp. 192–236, 1974. [81] O. Frank and D. Strauss, “Markov graphs,” Journal of the American Statistical Association, vol. 81, no. 395, pp. 832–842, 1986. [82] M. S. Handcock and J. H. Jones, “Likelihoodbased inference for stochastic models of sexual network formation,” Theoretical Population Biology, vol. 65, no. 4, pp. 413–422, 2004. [83] G. Robins, P. Pattison, and J. Woolcock, “Missing data in networks: exponential random graph (p∗) models for networks with nonrespondents,” Social Networks, vol. 26, no. 3, pp. 257–283, 2004. [84] G. Robins, T. Snijders, P. Wang, M. Handcock, and P. Pattison, “Recent developments in exponential random graph (p∗)models for social networks,” Social Networks, vol. 29, no. 2, pp. 192–215, 2007. [85] S. M. Goodreau, “Advances in exponential randomgraph (p∗)models applied to a large social network,” Social Networks, vol. 29, no. 2, pp. 231–248, 2007. [86] R. Albert and A. L. Barab´asi, “Statistical mechanics of complex networks,” Reviews of Modern Physics, vol. 74, no. 1, pp. 47–97, 2002. [87] R. PastorS 
URI:  http://wrap.warwick.ac.uk/id/eprint/34587 
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