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Volumn , Issue , 2015, Pages 111-130

Public health

Author keywords

[No Author keywords available]

Indexed keywords

BIOMEDICAL ENGINEERING; DISEASES; HEALTH; INSURANCE; PUBLIC HEALTH; SOCIAL NETWORKING (ONLINE); VIRUSES;

EID: 84954209998     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781316182635.004     Document Type: Chapter
Times cited : (10)

References (53)
  • 1
    • 76049088642 scopus 로고    scopus 로고
    • Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
    • Aral, Muchnik. L. and Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of National Academy of Science USA, 106, 21544-9.
    • (2009) Proceedings of National Academy of Science USA , vol.106 , pp. 21544-21549
    • Aral, M.L.1    Sundararajan, A.2
  • 5
    • 0033515256 scopus 로고    scopus 로고
    • Ten great public health achievements - united states. 1900-1999
    • Centers for Disease Control and Prevention (CDC)
    • Centers for Disease Control and Prevention (CDC). (1999). Ten great public health achievements - United States. 1900-1999. MMWR Morbidity Mortality Weekly Report, 48, 241-3.
    • (1999) MMWR Morbidity Mortality Weekly Report , vol.48 , pp. 241-243
  • 7
    • 34547170053 scopus 로고    scopus 로고
    • The spread of obesity in a large social net-work over 32 years
    • Christakis, N. A., and Fowler, J. H. (2007). The spread of obesity in a large social net-work over 32 years. New England Journal of Medicine, 357, 370-9.
    • (2007) New England Journal of Medicine , vol.357 , pp. 370-379
    • Christakis, N.A.1    Fowler, J.H.2
  • 8
    • 44249122799 scopus 로고    scopus 로고
    • The collective dynamics of smoking in a large scale social network
    • Christakis, N. A, and Fowler, J. H. (2008). The collective dynamics of smoking in a large scale social network. New England Journal of Medicine, 358, 2249-58.
    • (2008) New England Journal of Medicine , vol.358 , pp. 2249-2258
    • Christakis, N.A.1    Fowler, J.H.2
  • 9
    • 79956040653 scopus 로고    scopus 로고
    • Towards detecting influenza epidemics by analyzing twitter messages
    • ACM
    • Culotta, A. (2010.) Towards detecting influenza epidemics by analyzing Twitter messages. In ACM, Proceedings of the SOMA ‘10 (pp. 115-22). ACM.
    • (2010) ACM, Proceedings of the SOMA ‘10 , pp. 115-122
    • Culotta, A.1
  • 11
    • 84954204947 scopus 로고    scopus 로고
    • Potential of social media to determine hay fever seasons and drug efficacy
    • on One Health)
    • de Quincey, E., Kyriacou, T., Williams, N., and Pantin, T. (2014). Potential of social media to determine hay fever seasons and drug efficacy. Planet@Risk 2(4, Special Issue on One Health): 293-7.
    • (2014) Planet@Risk , vol.2 , pp. 293-297
    • De Quincey, E.1    Kyriacou, T.2    Williams, N.3    Pantin, T.4
  • 12
    • 70349264262 scopus 로고    scopus 로고
    • How the media reported the first day of the pandemic h1n1 2009: Results of eu-wide media analysis
    • Duncan, B. (2009). How the media reported the first day of the pandemic H1N1 2009: results of EU-wide media analysis. Eurosurveillance, 14(30): 1-3. http://www.eurosurveillance.org/images/dynamic/EE/V14N30/art19286.pdf.
    • (2009) Eurosurveillance , vol.14 , Issue.30 , pp. 1-3
    • Duncan, B.1
  • 13
    • 74049147542 scopus 로고    scopus 로고
    • Available at
    • Fox, S. (2009). The social life of health information. Available at http://www.pewinternet.org/Reports/2009/8-The-Social-Life-of-Health-Information/1 4-About-Us-Methodology.aspx?view=all.
    • (2009) The Social Life of Health Information
    • Fox, S.1
  • 15
    • 80053345545 scopus 로고    scopus 로고
    • Diurnal and seasonal mood vary with work, sleep, and daylength across different cultures
    • Golder, Scott A., et al. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across different cultures. Science, 333, 18-78 doi: 10.1126/science.1202775.
    • (2011) Science , vol.333 , pp. 18-78
    • Golder, S.A.1
  • 17
    • 84891813761 scopus 로고    scopus 로고
    • Influenza-like illness surveillance on twitter through auto-mated learning of naive language
    • Gesualdo, F., Stilo, G., Agricola, E., Gonfiantini, M. V., Pandolfi, E., Velardi, P., and Tozzi, A. E. (2014). Influenza-like illness surveillance on Twitter through auto-mated learning of naive language. PLOS ONE, 8(12), e82489.
    • (2014) PLOS ONE , vol.8 , Issue.12 , pp. e82489
    • Gesualdo, F.1    Stilo, G.2    Agricola, E.3    Gonfiantini, M.V.4    Pandolfi, E.5    Velardi, P.6    Tozzi, A.E.7
  • 18
    • 84876918884 scopus 로고    scopus 로고
    • Big data opportunities for global infectious disease surveillance
    • Hay, S. I., George, D. B., Moyer, C. L., and Brownstein, J. S. (2013). Big data opportunities for global infectious disease surveillance. PLOS Medicine, 10(4), e100-1413.
    • (2013) PLOS Medicine , vol.10 , Issue.4 , pp. e100-1413
    • Hay, S.I.1    George, D.B.2    Moyer, C.L.3    Brownstein, J.S.4
  • 19
    • 0041526787 scopus 로고    scopus 로고
    • Measles outbreaks in a population with declining vaccine update
    • Jansen, V. A. A., et al. (2003). Measles outbreaks in a population with declining vaccine update. Science, 301, 804.
    • (2003) Science , vol.301 , pp. 804
    • Jansen, V.1
  • 20
    • 80053510007 scopus 로고    scopus 로고
    • Self-diagnosis of influenza during a pandemic: A cross-sectional survey
    • Jutel, A., Baker, M. G., Stanley, J., Huang, Q. S., and Bandaranayake, D. (2011). Self-diagnosis of influenza during a pandemic: a cross-sectional survey. BMJ Open, 1:000-234. dio:10.1136/bjmopen-2011-000234.
    • (2011) BMJ Open , vol.1 , pp. 000-234
    • Jutel, A.1    Baker, M.G.2    Stanley, J.3    Huang, Q.S.4    Bandaranayake, D.5
  • 21
    • 33746196621 scopus 로고    scopus 로고
    • Different approaches to gathering epidemic intelligence in europe
    • Kaiser, R., and Coulombier, D. (2006). Different approaches to gathering epidemic intelligence in Europe. Eurosurveillance, 11(17), pii=29-48.
    • (2006) Eurosurveillance , vol.11 , Issue.17 , pp. 29-48
    • Kaiser, R.1    Coulombier, D.2
  • 22
    • 33746626068 scopus 로고    scopus 로고
    • What is epidemic intelligence, and how it is being improved in europe?
    • Kaiser, R., Coulombier, D., Maldari, M., Morgan, D., and Paquet, C. (2006). What is epidemic intelligence, and how it is being improved in Europe? Eurosureillance, 11(2), 060-202.
    • (2006) Eurosureillance , vol.11 , Issue.2 , pp. 060-202
    • Kaiser Coulombier, R.D.1    Maldari, M.2    Morgan, D.3    Paquet, C.4
  • 23
    • 84893046729 scopus 로고    scopus 로고
    • A roadmap to integrated digital public health surveillance: The vision and the challenges
    • International World Wide Web Conferences Steering Committee
    • Kostkova, P. (2013). A roadmap to integrated digital public health surveillance: the vision and the challenges. In Proceedings of the 22nd International Conference on World Wide Web Companion (WWW ‘13 Companion) (pp. 687-94). International World Wide Web Conferences Steering Committee.
    • (2013) Proceedings of the 22Nd International Conference on World Wide Web Companion (WWW ‘13 Companion) , pp. 687-694
    • Kostkova, P.1
  • 24
    • 84883417027 scopus 로고    scopus 로고
    • Major infection events over 5 years: How is media coverage influencing online information needs of health care professionals and the public?
    • Kostkova, P., Fowler, D., Wiseman, S., and Weinberg, J. R. (2013). Major infection events over 5 years: how is media coverage influencing online information needs of health care professionals and the public? Journal of Medical Internet Research (JMIR), 15(7), e107 doi:10.2196/jmir.2146.
    • (2013) Journal of Medical Internet Research (JMIR) , vol.15 , Issue.7 , pp. e107
    • Kostkova Fowler, P.D.1    Wiseman, S.2    Weinberg, J.R.3
  • 27
    • 84905728097 scopus 로고    scopus 로고
    • #swineflu: The use of twitter as an early warning and risk communication tool in the 2009 swine flu pandemic
    • Kostkova, P., Szomszor, M., and St Luis, C. (2014). #swineflu: the use of Twitter as an early warning and risk communication tool in the 2009 swine flu pandemic. ACM Transactions on Management Information Systems, 5(2), Article 8.
    • (2014) ACM Transactions on Management Information Systems , vol.5 , Issue.2
    • Kostkova, P.1    Szomszor, M.2    St Luis, C.3
  • 31
    • 84867410703 scopus 로고    scopus 로고
    • Nowcasting events from the social web with statistical learning
    • Lampos, V., and Cristianini, N. (2012). Nowcasting events from the social web with statistical learning. ACM TISM, 3(4), Article 72.
    • (2012) ACM TISM , vol.3 , Issue.4
    • Lampos, V.1    Cristianini, N.2
  • 34
    • 79957933072 scopus 로고    scopus 로고
    • Which idea is likely to make the biggest impact on healthcare by 2020?
    • Malik, S. (2011.) Which idea is likely to make the biggest impact on healthcare by 2020? BMJ, 342: d1998 doi: 10.1136/bmj.d1998.
    • (2011) BMJ , vol.342 , pp. d1998
    • Malik, S.1
  • 35
    • 34249663235 scopus 로고    scopus 로고
    • Epidemic intelligence: A new framework for strengthening disease surveillance in europe
    • Paquet C., Coulombier, D., Kaiser, R., and Ciotti, M. (2006). Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Eurosurveillance 11(12), p=665.
    • (2006) Eurosurveillance , vol.11 , Issue.12 , pp. 665
    • Paquet, C.1    Coulombier, D.2    Kaiser, R.3    Ciotti, M.4
  • 38
    • 84864615762 scopus 로고    scopus 로고
    • Digital epidemiology
    • Salathe, M., et al. (2012). Digital epidemiology. PLoS Computational Biology, 8(7), e100-2616. dio:10.1371/journal.pcbi.1002616.
    • (2012) Plos Computational Biology , vol.8 , Issue.7 , pp. e100-2616
    • Salathe, M.1
  • 39
    • 84933503982 scopus 로고    scopus 로고
    • The dynamics of health behaviour sentiments on a large online social network
    • Salathe, M., Duy, Q. V., Shashank, K., and Hunter, D. R. (2013). The dynamics of health behaviour sentiments on a large online social network. EPJ Data Science, 2, 4.
    • (2013) EPJ Data Science , vol.2 , pp. 4
    • Salathe, M.1    Duy, Q.V.2    Shashank, K.3    Hunter, D.R.4
  • 41
    • 80055065085 scopus 로고    scopus 로고
    • Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control
    • Salathe, M., and Khandelwal, S. (2011). Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLOS Computational Biology, 7(10), e100-2199.
    • (2011) PLOS Computational Biology , vol.7 , Issue.10 , pp. e100-2199
    • Salathe, M.1    Khandelwal, S.2
  • 42
    • 79956152316 scopus 로고    scopus 로고
    • Twitter as medium and message
    • Savage, N. (2011). Twitter as medium and message. Communications of the ACM, 54(3), 8-20.
    • (2011) Communications of the ACM , vol.54 , Issue.3 , pp. 8-20
    • Savage, N.1
  • 43
    • 77949891085 scopus 로고    scopus 로고
    • Dissemination of health information through social networks: Twitter and antibiotics
    • Scanfeld, D., Scanfeld, V., and Larson, E. L. (2010). Dissemination of health information through social networks: Twitter and antibiotics. AJIC: American Journal of Infection Control, 3(8), 182-8.
    • (2010) AJIC: American Journal of Infection Control , vol.3 , Issue.8 , pp. 182-188
    • Scanfeld, D.1    Scanfeld, V.2    Larson, E.L.3
  • 44
    • 79956110272 scopus 로고    scopus 로고
    • Homophily and contagion are generically confounded in observational social network studies
    • Shalizi, C. R., and Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociology Methods Research, 40, 211-39.
    • (2011) Sociology Methods Research , vol.40 , pp. 211-239
    • Shalizi, C.R.1    Thomas, A.C.2
  • 45
    • 79955757514 scopus 로고    scopus 로고
    • The use of twitter to track levels of disease activity and public health concern in the u.S. during the influenza a h1n1 pandemic
    • Signorini, A., Segre, A. M., and Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public health concern in the U.S. during the influenza A H1N1 pandemic. POS One, 6(5), e19467 doi: 10.1371/journal.pone.0019467.
    • (2011) POS One , vol.6 , Issue.5 , pp. e19467
    • Signorini, A.1    Segre, A.M.2    Polgreen, P.M.3
  • 47
    • 84862497423 scopus 로고    scopus 로고
    • Can twitter predict disease outbreaks?
    • Available online at
    • St Louis, C., and Zorlu, G. (2012). Can Twitter predict disease outbreaks? BMJ, 344, e2353. Available online at http://www.bmj.com/content/344/bmj.e2353.
    • (2012) BMJ , vol.344 , pp. e2353
    • St Louis, C.1    Zorlu, G.2
  • 52
    • 84859129524 scopus 로고    scopus 로고
    • Risk perception and information-seeking behaviour during the 2009-10 influenza a (H1n1) pdm09 pandemic in germany
    • Walter, D., Bohmer, M. M., Reiter, S., Krause, G., and Wichmann, O. (2012). Risk perception and information-seeking behaviour during the 2009-10 influenza A (H1N1) PDM09 pandemic in Germany. Eurosurveillance, 7(13), 1-8.
    • (2012) Eurosurveillance , vol.7 , Issue.13 , pp. 1-8
    • Walter, D.1    Bohmer, M.M.2    Reiter, S.3    Krause, G.4    Wichmann, O.5
  • 53


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.