연구동향 분석
박사

Development of Influenza Surveillance Model Based on Internet Search Query and Social Media Data : 인터넷 검색쿼리와 소셜미디어 데이터를 활용한 사회인구학적 독감 감시모형개발

우혜경 2015년

논문상세정보
인용/피인용
' Development of Influenza Surveillance Model Based on Internet Search Query and Social Media Data : 인터넷 검색쿼리와 소셜미디어 데이터를 활용한 사회인구학적 독감 감시모형개발' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • big data
  • early response
  • epidemiology
  • forecasting
  • influenza
  • infodemiology
  • infoveillance
  • internet search query
  • population surveillance
  • social media
  • surveillance
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
1,485 0

0.0%

' Development of Influenza Surveillance Model Based on Internet Search Query and Social Media Data : 인터넷 검색쿼리와 소셜미디어 데이터를 활용한 사회인구학적 독감 감시모형개발' 의 참고문헌

  • Yuan, Q., et al., Monitoring influenza epidemics in china with search queryfrom baidu. PLoS One, 2013. 8(5): p. e64323.
  • Winslow, C.E., THE UNTILLED FIELDS OF PUBLIC HEALTH. Science, 1920.51(1306): p. 23-33.
  • WebCite. Market share of search engine in South Korea. . 2015 [cited2015 April 05]; Available from: http://www.webcitation.org/6QrnPuRwJ.
  • Velasco, E., et al., Social media and internet-based data in global systemsfor public health surveillance: a systematic review. Milbank Q, 2014. 92(1):p. 7-33.
  • Seo, D.W., et al., Cumulative query method for influenza surveillance usingsearch engine data. J Med Internet Res, 2014. 16(12): p. e289.
  • Scanfeld, D., V. Scanfeld, and E.L. Larson, Dissemination of healthinformation through social networks: twitter and antibiotics. Am J InfectControl, 2010. 38(3): p. 182-8.
  • Santos, J.C. and S. Matos, Analysing Twitter and web queries for flu trendprediction. Theoretical Biology and Medical Modelling, 2014. 11(Suppl 1):p. S6.
  • Saeys, Y., I. Inza, and P. Larranaga, A review of feature selectiontechniques in bioinformatics. bioinformatics, 2007. 23(19): p. 2507-2517.
  • Rice, R.E., Influences, usage, and outcomes of Internet health informationsearching: multivariate results from the Pew surveys. Int J Med Inform,2006. 75(1): p. 8-28.
  • Prieto, V.M., et al., Twitter: a good place to detect health conditions. PLoSOne, 2014. 9(1): p. e86191.
  • Polgreen, P.M., et al., Using internet searches for influenza surveillance. Clin Infect Dis, 2008. 47(11): p. 1443-8.
  • Pawelek, K.A., A. Oeldorf-Hirsch, and L. Rong, Modeling the impact oftwitter on influenza epidemics. Math Biosci Eng, 2014. 11(6): p. 1337-56.
  • Paul, M.J., M. Dredze, and D. Broniatowski, Twitter improves influenzaforecasting. PLoS Curr, 2014. 6.
  • Park, S. and E. Cho, National Infectious Diseases Surveillance data ofSouth Korea. Epidemiol Health, 2014. 36: p. e2014030.
  • Mykhalovskiy, E. and L. Weir, The Global Public Health IntelligenceNetwork and early warning outbreak detection: a Canadian contribution toglobal public health. Can J Public Health, 2006. 97(1): p. 42-4.
  • Milinovich, G.J., et al., Using internet search queries for infectious diseasesurveillance: screening diseases for suitability. BMC Infect Dis, 2014.14(1): p. 3840.
  • Milinovich, G.J., et al., Internet-based surveillance systems for monitoringemerging infectious diseases. Lancet Infect Dis, 2014. 14(2): p. 160-8.
  • Madoff, L.C., ProMED-mail: an early warning system for emergingdiseases. Clin Infect Dis, 2004. 39(2): p. 227-32.
  • Li, F., Y. Yang, and E.P. Xing. From lasso regression to feature vectormachine. in Advances in Neural Information Processing Systems. 2005.
  • Lazer, D., et al., Twitter: big data opportunities--response. Science, 2014.345(6193): p. 148-9.
  • Lazer, D., et al., Big data. The parable of Google Flu: traps in big dataanalysis. Science, 2014. 343(6176): p. 1203-5.
  • Keller, M., et al., Use of unstructured event-based reports for globalinfectious disease surveillance. Emerg Infect Dis, 2009. 15(5): p. 689-95.
  • Kass-Hout, T.A. and H. Alhinnawi, Social media in public health. Br MedBull, 2013. 108: p. 5-24.
  • Kang, M., et al., Using Google trends for influenza surveillance in SouthChina. PloS one, 2013. 8(1): p. e55205.
  • KISDI, KISDI STAT Report (13-04): Current use of SNS 2013: Seoul, Korea.
  • KISA, M.a., Survey on the Internet Usage, Korea, M.a. KISA, Editor. 2013.12, MSIP and KISA: Seoul, Korea.
  • ITU, The World in 2014: ICT Facts and Figures, 2014, in ICT Facts andFigures, 2014. 2014, International Telecommunication Union: Geneva,Switwerland.
  • Hulth, A., G. Rydevik, and A. Linde, Web queries as a source for syndromicsurveillance. PloS one, 2009. 4(2): p. e4378.
  • Hulth, A. and G. Rydevik, Web query-based surveillance in Sweden duringthe influenza A (H1N1) 2009 pandemic, April 2009 to February 2010. EuroSurveill, 2011. 16(18): p. -.
  • Hulth, A. and G. Rydevik, GET WELL: an automated surveillance systemfor gaining new epidemiological knowledge. BMC Public Health, 2011. 11:p. 252.
  • Heymann, D.L. and G.R. Rodier, Hot spots in a wired world: WHOsurveillance of emerging and re-emerging infectious diseases. Lancet InfectDis, 2001. 1(5): p. 345-53.
  • Herman Tolentino, M., et al., Scanning the emerging infectious diseaseshorizon-visualizing ProMED emails using EpiSPIDER. Advances in diseasesurveillance, 2007. 2: p. 169.
  • He, Q., et al. Web query recommendation via sequential query prediction.in Data Engineering, 2009. ICDE'09. IEEE 25th International Conferenceon. 2009. IEEE.
  • Guyon, I. and A. Elisseeff, An introduction to variable and featureselection. The Journal of Machine Learning Research, 2003. 3: p. 1157-1182.
  • Gu, H., et al., Importance of Internet surveillance in public healthemergency control and prevention: evidence from a digital epidemiologicstudy during avian influenza A H7N9 outbreaks. J Med Internet Res, 2014.16(1): p. e20.
  • Ginsberg, J., et al., Detecting influenza epidemics using search engine querydata. Nature, 2009. 457(7232): p. 1012-4.
  • Freifeld, C.C., et al., HealthMap: global infectious disease monitoringthrough automated classification and visualization of Internet media reports.J Am Med Inform Assoc, 2008. 15(2): p. 150-7.
  • Corley, C.D., et al., Using Web and social media for influenza surveillance.Adv Exp Med Biol, 2010. 680: p. 559-64.
  • Cook, S., et al., Assessing Google flu trends performance in the UnitedStates during the 2009 influenza virus A (H1N1) pandemic. PloS one, 2011.6(8): p. e23610.
  • Collier, N., et al., BioCaster: detecting public health rumors with a Webbasedtext mining system. Bioinformatics, 2008. 24(24): p. 2940-1.
  • Cho, S., et al., Correlation between national influenza surveillance dataand google trends in South Korea. PLoS One, 2013. 8(12): p. e81422.
  • Chew, C. and G. Eysenbach, Pandemics in the age of Twitter: contentanalysis of Tweets during the 2009 H1N1 outbreak. PloS one, 2010. 5(11):p. e14118.
  • CDC. Seasnoal Influenza (Flu):Flu Basics-Influenza Symptoms. 2015March 1, 2015]; Available from:http://www.cdc.gov/flu/about/disease/symptoms.htm.
  • Brownstein, J.S., et al., Surveillance Sans Frontieres: Internet-basedemerging infectious disease intelligence and the HealthMap project. PLoSMed, 2008. 5(7): p. e151.
  • Broniatowski, D.A., M.J. Paul, and M. Dredze, Twitter: big dataopportunities. Science, 2014. 345(6193): p. 148.
  • Broniatowski, D.A., M.J. Paul, and M. Dredze, National and local influenzasurveillance through twitter: An analysis of the 2012-2013 influenzaepidemic. PloS one, 2013. 8(12): p. e83672.
  • Baeza-Yates, R., C. Hurtado, and M. Mendoza. Query recommendationusing query logs in search engines. in Current Trends in DatabaseTechnology-EDBT 2004 Workshops. 2005. Springer.
  • Althouse, B.M., Y.Y. Ng, and D.A. Cummings, Prediction of dengueincidence using search query surveillance. PLoS neglected tropical diseases,2011. 5(8): p. e1258.