1.Tellier, R. Review of aerosol transmission of influenza a virus. Emerging Infectious Diseases 2006; 12: 1657–1662.
2.Brankston, G, et al. Transmission of influenza a in human beings. Lancet Infectious Diseases 2007; 7: 257–265.
3.Musher, DM. How contagious are common respiratory tract infections? New England Journal of Medicine 2003; 348: 1256–1266.
4.Rea, E, et al. Duration and distance of exposure are important predictors of transmission among community contacts of Ontario SARS cases. Epidemiology and Infection 2007; 135: 914–921.
5.Yang, W, Elankumaran, S, Marr, LC. Concentrations and size distributions of airborne influenza a viruses measured indoors at a health centre, a day-care centre and on aeroplanes. Journal of the Royal Society Interface 2011; 8: 1176–1184.
6.Stilianakis, NI, Drossinos, Y. Dynamics of infectious disease transmission by inhalable respiratory droplets. Journal of the Royal Society Interface 2010; 7: 1355–1366.
7.Weber, TP, Stilianakis, NI. Inactivation of influenza a viruses in the environment and modes of transmission: a critical review. Journal of Infection 2008; 57: 361–373.
8.Tellier, R. Aerosol transmission of influenza a virus: a review of new studies. Journal of the Royal Society Interface 2009; 6 (Suppl. 6): S783–790.
9.Chen, SC, et al. Viral kinetics and exhaled droplet size affect indoor transmission dynamics of influenza infection. Indoor Air 2009; 19: 401–413.
10.Gralton, J, et al. The role of particle size in aerosolised pathogen transmission: a review. Journal of Infection 2011; 62: 1–13.
11.Fleisher, GR, Pasquariello, PS, Warren, WS. Intrafamilial transmission of Epstein-Barr virus infections. Journal of Pediatrics 1981; 98: 16–19.
12.Odegaard, K. Kissing as a mode of transmission of infectious mononucleosis. Lancet 1967; 1: 1052–1053.
13.Rhoads, MP, Magaret, AS, Zerr, DM. Family saliva sharing behaviors and age of human herpesvirus-6b infection. Journal of Infection 2007; 54: 623–626.
14.Li, S, et al. Dynamics and control of infections transmitted from person to person through the environment. American Journal of Epidemiology 2009; 170: 257–265.
15.Boone, SA, Gerba, CP. Significance of fomites in the spread of respiratory and enteric viral disease. Applied and Environmental Microbiology 2007; 73: 1687–1696.
16.Winther, B, et al. Environmental contamination with rhinovirus and transfer to fingers of healthy individuals by daily life activity. Journal of Medical Virology 2007; 79: 1606–1610.
17.Weber, TP, Stilianakis, NI. Inactivation of influenza a viruses in the environment and modes of transmission: a critical review. Journal of Infection 2008; 57: 361–373.
18.Jones, RM. Critical review and uncertainty analysis of factors influencing influenza transmission. Risk Analysis 2011; 31: 1226–1242.
19.Rheinbaben, F, et al. Transmission of viruses via contact in ahousehold setting: Experiments using bacteriophage straight phix174 as a model virus. Journal of Hospital Infection 2000; 46: 61–66.
20.Keeling, MJ, Eames, KT. Networks and epidemic models. Journal of the Royal Society Interface 2005; 2: 295–307.
21.Britton, T, Nordvik, MK, Liljeros, F. Modelling sexually transmitted infections: the effect of partnership activity and number of partners on r 0. Theoretical Population Biology 2007; 72: 389–399.
22.Nordvik, MK, Liljeros, F. Number of sexual encounters involving intercourse and the transmission of sexually transmitted infections. Sexually Transmitted Diseases 2006; 33: 342–349.
23.Wasserman, S, Faust, K. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press, 1994.
24.Read, JM, Eames, KT, Edmunds, WJ. Dynamic social networks and the implications for the spread of infectious disease. Journal of the Royal Society Interface 2008; 5: 1001–1007.
25.Smieszek, T. A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread. Theoretical Biology and Medical Modelling 2009; 6.
26.Kretzschmar, M, Morris, M. Measures of concurrency in networks and the spread of infectious disease. Mathematical Biosciences 1996; 133: 165–195.
27.Eames, KTD, Keeling, MJ. Monogamous networks and the spread of sexually transmitted diseases. Mathematical Biosciences 2004; 189: 115–130.
28.Szendroi, B, Csányi, G. Polynomial epidemics and clustering in contact networks. Proceedings of the Royal Society, Series B: Biological Sciences 2004; 271: S364–S366.
29.Eames, KTD. Modelling disease spread through random and regular contacts in clustered populations. Theoretical Population Biology 2008; 73: 104–111.
30.Smieszek, T, Fiebig, L, Scholz, RW. Models of epidemics: When contact repetition and clustering should be included. Theoretical Biology and Medical Modelling 2009; 6.
31.Kasper, C, Voelkl, B. A social network analysis of primate groups. Primates 2009; 50: 343–356.
32.Hamede, RK, et al. 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 2009; 12: 1147–1157.
33.Ochs, E, et al. Video ethnography and ethnoarchaeological tracking. In: Pitt-Catsouphes, M, Kossek, EE, Sweet, S, eds. The Work and Family Handbook: Multi-disciplinary Perspectives and Approaches. New Jersey: Erlbaun Associates Inc., 2003, pp. 387–409.
34.Helbing, D, et al. Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 2001; 28: 361–383.
35.Polgreen, PM, et al. Prioritizing healthcare worker vaccinations on the basis of social network analysis. Infection Control and Hospital Epidemiology 2010; 31: 893–900.
36.Villaseñor-Sierra, A, Quinñonez-Alvarado, MG, Caballero-Hoyos, JR. Interpersonal relationships and group a streptococcus spread in a Mexican day-care center. Salud Publica de Mexico 2007; 49: 323–329.
37.Ge, W, Collins, RT, Ruback, B. Automatically detecting the small group structure of a crowd. In: 2009 Workshop on Applications of Computer Vision (WACV). Snowbird, UT, 2009, pp. 1–8.
38.Ali, S, Shah, M. Floor fields for tracking in high density crowd scenes. In: 10th European Conference on Computer Vision, ECCV 2008. Marseille, 2008, pp. 1–14.
39.Moussaid, M, et al. Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society, Series B: Biological Sciences 2009; 276: 2755–2762.
40.Gavrila, DM. The visual analysis of human movement: a survey. Computer Vision and Image Understanding 1999; 73: 82–98.
41.Cheriyadat, AM, Radke, RJ. Detecting dominant motions in dense crowds. IEEE Journal on Selected Topics in Signal Processing 2008; 2: 568–581.
42.Doherty, AR, Moulin, CJA, Smeaton, AF. Automatically assisting human memory: a sensecam browser. Memory 2011; 19: 785–795.
43.Berry, E, et al. The use of a wearable camera, sensecam, as a pictorial diary to improve autobiographical memory in a patient with limbic encephalitis: a preliminary report. Neuropsychological Rehabilitation 2007; 17: 582–601.
44.Fu, Y-c.Measuring personal networks with daily contacts: a single-item survey question and the contact diary. Social Networks 2005; 27: 169–186.
45.Fu, Yc.Contact diaries: building archives of actual and comprehensive personal networks. Field Methods 2007; 19: 194–217.
46.Marsden, PV. Network data and measurement. Annual Review of Sociology 1990; 16: 435–463.
47.de Sola Pool, I, Kochen, M. Contacts and influence. Social Networks 1978; 1: 5–51.
48.Bernard, HR, et al. Comparing four different methods for measuring personal social networks. Social Networks 1990; 12: 179–215.
49.Edmunds, WJ, O'Callaghan, CJ, Nokes, DJ. Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections. Proceedings of the Royal Society, Series B: Biological Sciences 1997; 264: 949–957.
50.Beutels, P, et al. Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface. Epidemiology and Infection 2006; 134: 1158–1166.
51.Edmunds, WJ, et al. Mixing patterns and the spread of close-contact infectious diseases. Emerging Themes in Epidemiology 2006; 3: 10.
52.Mikolajczyk, RT, Kretzschmar, M. Collecting social contact data in the context of disease transmission: prospective and retrospective study designs. Social Networks 2008; 30: 127–135.
53.Glass, LM, Glass, RJ. Social contact networks for the spread of pandemic influenza in children and teenagers. BMC Public Health 2008; 8: 61.
54.Mikolajczyk, RT, et al. Social contacts of school children and the transmission of respiratory-spread pathogens. Epidemiology and Infection 2008; 136: 813–822.
55.Conlan, AJ, et al. Measuring social networks in British primary schools through scientific engagement. Proceedings of the Royal Society, Series B: Biological Sciences 2011; 278: 1467–1475.
56.Eames, KTD, et al. The impact of illness and the impact of school closure on social contact patterns. Health Technology Assessment 2010; 14: 267–312.
57.Eames, KT, Tilston, NL, Edmunds, WJ. The impact of school holidays on the social mixing patterns of school children. Epidemics 2011; 3: 103–108.
58.Bernard, H, et al. Nurses' contacts and potential for infectious disease transmission. Emerging Infectious Diseases 2009; 15: 1438–1444.
59.Wallinga, J, Teunis, P, Kretzschmar, M. Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. American Journal of Epidemiology 2006; 164: 936–944.
60.Mossong, J, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine 2008; 5: 0381–0391.
61.DeStefano, F, et al. Factors associated with social contacts in four communities during the 2007–2008 influenza season. Epidemiology and Infection 2011; 139: 1181–1190.
62.Horby, P, et al. Social contact patterns in vietnam and implications for the control of infectious diseases. PLoS One 2011; 6.
63.Smieszek, T, et al. Collecting close-contact social mixing data with contact diaries: reporting errors and biases. Epidemiology and Infection 2012; 140: 744–752.
64.McCaw, JM, et al. Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks. BMC Infectious Diseases 2010; 10: 166.
65.Rohani, P, Zhong, X, King, AA. Contact network structure explains the changing epidemiology of pertussis. Science 2010; 330: 982–985.
66.Birrell, PJ, et al. Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. Proceedings of the National Academy of Sciences USA 2011; 108: 18238–18243.
67.Goeyvaerts, N, et al. Estimating infectious disease parameters from data on social contacts and serological status. Journal of the Royal Statistical Society, Series C: Applied Statistics 2010; 59: 255–277.
68.Kretzschmar, M, Teunis, PFM, Pebody, RG. Incidence and reproduction numbers of pertussis: estimates from serological and social contact data in five European countries. PLoS Medicine 2010; 7: e1000291.
69.Hens, N, et al. Mining social mixing patterns for infectious disease models based on a two-day population survey in belgium. BMC Infectious Diseases 2009; 9: 5.
70.Melegaro, A, et al. What types of contacts are important for the spread of infections?: Using contact survey data to explore European mixing patterns. Epidemics 2011; 3: 143–151.
71.Weatherall, J, Jones, A. Ubiquitous networks and their applications. IEEE Wireless Communications 2002; 9: 18–29.
72.Hui, P, et al. Pocket switched networks and human mobility in conference environments. In: ACM SIGCOMM 2005 Workshops: Conference on Computer Communications. Philadelphia, PA, 2005, pp. 244–251.
73.Isella, L, et al. What's in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology 2010.
74.Isella, L, et al. Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS One 2011; 6.
75.Stehlé, J, et al. High-resolution measurements of face-to-face contact patterns in a primary school. PLoS One 2011; 6.
76.Cattuto, C, et al. Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One 2010; 5.
77.Salathé, M, et al. A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences USA 2010; 107: 22020–22025.
78.Eagle, N, Pentland, A. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 2006; 10: 255–268.
79.Eagle, N, Pentland, AS, Lazer, D. Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences USA 2009; 106: 15274–15278.
80.Wang, Y, Krishnamachari, B, Valente, TW. Findings from an empirical study of fine-grained human social contacts. In: 6th International Conference on Wireless On-demand Network Systems and Services, WONS 2009. Snowbird, UT, 2009, pp. 153–160.
81.Sastry, N, et al. Data delivery properties of human contact networks. IEEE Transactions on Mobile Computing 2011; 10: 868–880.
82.McNett, M, Voelker, GM. Access and mobility of wireless pda users. Mobile Computing and Communications Review 2005; 9: 40–55.
83.Longini, IM Jr., et al. Containing pandemic influenza at the source. Science 2005; 309: 1083–1087.
84.Ferguson, NM, et al. Strategies for mitigating an influenza pandemic. Nature 2006; 442: 448–452.
85.Germann, TC, et al. Mitigation strategies for pandemic influenza in the United States. Proceedings of the National Academy of Sciences USA 2006; 103: 5935–5940.
86.Riley, S, Ferguson, NM. Smallpox transmission and control: spatial dynamics in Great Britain. Proceedings of the National Academy of Sciences USA 2006; 103: 12637–12642.
87.Eubank, S, et al. Modelling disease outbreaks in realistic urban social networks. Nature 2004; 429: 180–184.
88.Del Valle, SY, et al. Mixing patterns between age groups in social networks. Social Networks 2007; 29: 539–554.
89.Yang, Y, Atkinson, PM. Individual space–time activity-based model: a model for the simulation of airborne infectious-disease transmission by activity-bundle simulation. Environment and Planning B: Planning and Design 2008; 35: 80–99.
90.Zagheni, E, et al. Using time-use data to parameterize models for the spread of close-contact infectious diseases. American Journal of Epidemiology 2008; 168: 1082–1090.
91.Iozzi, F, et al. Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data. PLoS Computational Biology 2010; 6.
92.Yang, Y, Atkinson, P, Ettema, D. Individual space-time activity-based modelling of infectious disease transmission within a city. Journal of the Royal Society Interface 2008; 5: 759–772.
93.Gonzalez, MC, Hidalgo, CA, Barabasi, AL. Understanding individual human mobility patterns. Nature 2008; 453: 779–782.
94.Eames, KT, Read, JM, Edmunds, WJ. Epidemic prediction and control in weighted networks. Epidemics 2009; 1: 70–76.
95.Travers, J, Milgram, S. An experimental study of the small world problem. Sociometry 1967; 32: 425–443.
96.Kleinfeld, JS. The small world problem. Society 2002; 39: 61–66.
97.Granovetter, MS. The strength of weak ties. American Journal of Sociology 1973; 78: 1360–1380.
98.Watts, DJ, Strogatz, SH. Collective dynamics of ‘small-world’ networks. Nature 1998; 393: 440–442.
99.Newman, MEJ. The structure and function of complex networks. SIAM Review 2003; 45: 167–256.
100.Salathé, M, Jones, JH. Dynamics and control of diseases in networks with community structure. PLoS Computational Biology 2010; 6.
101.Funk, S, et al. The spread of awareness and its impact on epidemic outbreaks. Proceedings of the National Academy of Sciences USA 2009; 106: 6872–6877.
102.Eames, KT. Networks of influence and infection: parental choices and childhood disease. Journal of the Royal Society Interface 2009; 6: 811–814.
103.Bansal, S, et al. The dynamic nature of contact networks in infectious disease epidemiology. Journal of Biological Dynamics 2010; 4: 478–489.
104.Bootsma, MC, Ferguson, NM. The effect of public health measures on the 1918 influenza pandemic in U.S. Cities. Proceedings of the National Academy of Sciences USA 2007; 104: 7588–7593.
105.Hatchett, RJ, Mecher, CE, Lipsitch, M. Public health interventions and epidemic intensity during the 1918 influenza pandemic. Proceedings of the National Academy of Sciences USA 2007; 104: 7582–7587.
106.Bates, SJ, et al. Relating diarrheal disease to social networks and the geographic configuration of communities in rural ecuador. American Journal of Epidemiology 2007; 166: 1088–1095.
107.Rothenberg, R, et al. Social and geographic distance in HIV risk. Sexually Transmitted Diseases 2005; 32: 506–512.
108.Caley, P, Philp, DJ, McCracken, K. Quantifying social distancing arising from pandemic influenza. Journal of the Royal Society Interface 2008; 5: 631–639.
109.Van Druten, JAM, et al. Mumps, measles and rubella, a longitudinal serological study into degree of protection and risk of infection [in Dutch]. (Eindverslag Praeventiefondsproject no. 28-1348). The Hague, the Netherlands: Praeventiefonds, 1990.
110.Groendyke, C, Welch, D, Hunter, DR. Bayesian inference for contact networks given epidemic data. Scandinavian Journal of Statistics 2011; 38: 600–616.
111.Cauchemez, S, et al. Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proceedings of the National Academy of Sciences USA 2011; 108: 2825–2830.
112.Ionides, EL, Bretó, C, King, AA. Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences USA 2006; 103: 18438–18443.
113.Rhodes, CJ, Jones, P. Inferring missing links in partially observed social networks. Journal of the Operational Research Society 2009; 60: 1373–1383.
114.Potter, GE, et al. Estimating within household contact networks from egocentric data. Annals of Applied Statistics 2011; 5: 1816–1838.