메뉴 건너뛰기




Volumn , Issue , 2017, Pages 311-319

Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model

Author keywords

Computational epidemiology; Influenza modeling; Real time forecasting; Social media

Indexed keywords

DECISION MAKING; FORECASTING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SURVEYS; WORLD WIDE WEB;

EID: 85049376683     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3038912.3052678     Document Type: Conference Paper
Times cited : (71)

References (63)
  • 1
    • 85051533435 scopus 로고    scopus 로고
    • March
    • http://www.who.int/mediacentre/factsheets/fs211/en/, March 2014.
    • (2014)
  • 2
    • 0036100089 scopus 로고    scopus 로고
    • Medical care capacity for influenza outbreaks, Los Angeles
    • C.A. Glaser and et al. Medical care capacity for influenza outbreaks, Los Angeles. Emerging infectious diseases, 8(6):569–574, 2002.
    • (2002) Emerging Infectious Diseases , vol.8 , Issue.6 , pp. 569-574
    • Glaser, C.A.1
  • 3
    • 3042721544 scopus 로고    scopus 로고
    • Community influenza outbreaks and emergency department ambulance diversion
    • M.J. Schull, M.M. Mamdani, and J. Fang. Community influenza outbreaks and emergency department ambulance diversion. Annals of emergency medicine, 44(1):61–67, 2004.
    • (2004) Annals of Emergency Medicine , vol.44 , Issue.1 , pp. 61-67
    • Schull, M.J.1    Mamdani, M.M.2    Fang, J.3
  • 7
    • 84864615762 scopus 로고    scopus 로고
    • Digital epidemiology
    • M. Salathe and et al. Digital epidemiology. PLoS Comput Biol, 8(7):e1002616, 2012.
    • (2012) PLoS Comput Biol , vol.8 , Issue.7 , pp. e1002616
    • Salathe, M.1
  • 8
    • 84958762492 scopus 로고    scopus 로고
    • Enhancing disease surveillance with novel data streams: Challenges and opportunities
    • B. M. Althouse and et al. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Science, 4(1):17, 2015.
    • (2015) EPJ Data Science , vol.4 , Issue.1 , pp. 17
    • Althouse, B.M.1
  • 10
    • 84896056107 scopus 로고    scopus 로고
    • The parable of Google Flu: Traps in big data analysis
    • March
    • D. Lazer, R. Kennedy, G. King, and A. Vespignani. The parable of Google Flu: traps in big data analysis. Science, 343(14 March), 2014.
    • (2014) Science , vol.343 , Issue.14
    • Lazer, D.1    Kennedy, R.2    King, G.3    Vespignani, A.4
  • 11
    • 76049115282 scopus 로고    scopus 로고
    • Multiscale mobility networks and the spatial spreading of infectious diseases
    • D. Balcan, V. Colizza, B. Gonçalves, H. Hu, J.J. Ramasco, and A. Vespignan. Multiscale mobility networks and the spatial spreading of infectious diseases. PNAS, 106(51):21484–21489, 2009.
    • (2009) PNAS , vol.106 , Issue.51 , pp. 21484-21489
    • Balcan, D.1    Colizza, V.2    Gonçalves, B.3    Hu, H.4    Ramasco, J.J.5    Vespignan, A.6
  • 12
    • 70350687996 scopus 로고    scopus 로고
    • Seasonal transmission potential and activity peaks of the new influenza A(H1N1): A Monte Carlo likelihood analysis based on human mobility
    • D. Balcan and et al. Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility. BMC Medicine, 7(1):45, 2009.
    • (2009) BMC Medicine , vol.7 , Issue.1 , pp. 45
    • Balcan, D.1
  • 13
    • 77956419343 scopus 로고    scopus 로고
    • Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model
    • aug
    • D. Balcan, B. Gonçalves, H. Hu, J.J. Ramasco, V. Colizza, and A. Vespignani. Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. Journal of Computational Science, 1(3):132–145, aug 2010.
    • (2010) Journal of Computational Science , vol.1 , Issue.3 , pp. 132-145
    • Balcan, D.1    Gonçalves, B.2    Hu, H.3    Ramasco, J.J.4    Colizza, V.5    Vespignani, A.6
  • 16
    • 84979000412 scopus 로고    scopus 로고
    • Results from the centers for disease control and prevention’s predict the 2013–2014 influenza season challenge
    • M. Biggerstaff and et al. Results from the centers for disease control and prevention’s predict the 2013–2014 influenza season challenge. BMC Infectious Diseases, 16(1):357, 2016.
    • (2016) BMC Infectious Diseases , vol.16 , Issue.1 , pp. 357
    • Biggerstaff, M.1
  • 18
    • 33845508786 scopus 로고    scopus 로고
    • Inference for nonlinear dynamical systems
    • E.L. Ionides, C. Bretó, and A.A. King. Inference for nonlinear dynamical systems. PNAS, 103(49):18438–18443, 2006.
    • (2006) PNAS , vol.103 , Issue.49 , pp. 18438-18443
    • Ionides, E.L.1    Bretó, C.2    King, A.A.3
  • 19
    • 70350782519 scopus 로고    scopus 로고
    • Episimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks
    • IEEE Press
    • C.L. Barrett and et al. Episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing, page 37. IEEE Press, 2008.
    • (2008) Proceedings of The 2008 ACM/IEEE Conference on Supercomputing , pp. 37
    • Barrett, C.L.1
  • 20
    • 76749154557 scopus 로고    scopus 로고
    • Flute, a publicly available stochastic influenza epidemic simulation model
    • D.L. Chao, M.E. Halloran, V.J Obenchain, and I.M. Longini. Flute, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol, 6(1):e1000656, 2010.
    • (2010) PLoS Comput Biol , vol.6 , Issue.1 , pp. e1000656
    • Chao, D.L.1    Halloran, M.E.2    Obenchain, V.J.3    Longini, I.M.4
  • 21
    • 76249125029 scopus 로고    scopus 로고
    • The role of population heterogeneity and human mobility in the spread of pandemic influenza
    • S. Merler and M. Ajelli. The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proc. R. Soc. B, 277(1681):557–565, 2010.
    • (2010) Proc. R. Soc. B , vol.277 , Issue.1681 , pp. 557-565
    • Merler, S.1    Ajelli, M.2
  • 22
    • 60549098239 scopus 로고    scopus 로고
    • Detecting influenza epidemics using search engine query data
    • February
    • J. Ginsberg and et al. Detecting influenza epidemics using search engine query data. Nature, 457(7232):1012–1014, February 2009.
    • (2009) Nature , vol.457 , Issue.7232 , pp. 1012-1014
    • Ginsberg, J.1
  • 24
    • 84891941337 scopus 로고    scopus 로고
    • National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic
    • D.A. Broniatowski, M.J. Paul, and M. Dredze. National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic. PLOS ONE, 12(8):e83672, 2013.
    • (2013) PLOS ONE , vol.12 , Issue.8
    • Broniatowski, D.A.1    Paul, M.J.2    Dredze, M.3
  • 26
    • 84930606330 scopus 로고    scopus 로고
    • Forecasting the 2013-2014 influenza season using wikipedia
    • 05
    • K. Hickmann and et al. Forecasting the 2013-2014 influenza season using wikipedia. PLoS Comput Biol, 11(5):e1004239, 05 2015.
    • (2015) PLoS Comput Biol , vol.11 , Issue.5 , pp. e1004239
    • Hickmann, K.1
  • 27
    • 84901331477 scopus 로고    scopus 로고
    • Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time
    • D.J. McIver and J.S. Brownstein. Wikipedia usage estimates prevalence of influenza-like illness in the united states in near real-time. PLoS Comput Biol, 10(4):e1003581, 2014.
    • (2014) PLoS Comput Biol , vol.10 , Issue.4 , pp. e1003581
    • McIver, D.J.1    Brownstein, J.S.2
  • 28
    • 84977841553 scopus 로고    scopus 로고
    • On the ground validation of online diagnosis with twitter and medical records
    • T. Bodnar and et al. On the ground validation of online diagnosis with twitter and medical records. In WWW2014 Companion, pages 651–656, 2014.
    • (2014) WWW2014 Companion , pp. 651-656
    • Bodnar, T.1
  • 31
    • 84890239936 scopus 로고    scopus 로고
    • Real-time influenza forecasts during the 2012−2013 season
    • Dec
    • J. Shaman and et al. Real-time influenza forecasts during the 2012−2013 season. Nat. Comms, 4, Dec 2013.
    • (2013) Nat. Comms , vol.4
    • Shaman, J.1
  • 33
    • 84895745212 scopus 로고    scopus 로고
    • Evaluation of Internet-based dengue query data: Google Dengue Trends
    • R. Gluskin and et al. Evaluation of Internet-based dengue query data: Google Dengue Trends. PLoS Negl Trop Dis, 8(2):e2713, 2014.
    • (2014) PLoS Negl Trop Dis , vol.8 , Issue.2 , pp. e2713
    • Gluskin, R.1
  • 34
    • 84989912096 scopus 로고    scopus 로고
    • Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak
    • M.S. Majumder and et al. Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak. JMIR public health and surveillance, 2(1):e30, 2016.
    • (2016) JMIR Public Health and Surveillance , vol.2 , Issue.1 , pp. e30
    • Majumder, M.S.1
  • 35
    • 85051489907 scopus 로고    scopus 로고
    • Oct
    • http://ecdc.europa.eu/en/healthtopics/influenza/EISN/Pages/index.aspx, Oct. 2016.
    • (2016)
  • 36
    • 85051505211 scopus 로고    scopus 로고
    • Oct
    • http://ecdc.europa.eu/en/healthtopics/influenza/surveillance/Pages/influenza_case_ definitions.aspx, Oct. 2016.
    • (2016)
  • 39
  • 40
    • 85051465321 scopus 로고    scopus 로고
    • January
    • GPS Accuracy. http://www.gps.gov/systems/gps/performance/accuracy/, January 2014.
    • (2014)
  • 42
    • 85085133306 scopus 로고    scopus 로고
    • You are what you tweet: Analyzing twitter for public health
    • M.J. Paul and M. Dredze. You are what you tweet: Analyzing twitter for public health. ICWSM, 20:265–272, 2011.
    • (2011) ICWSM , vol.20 , pp. 265-272
    • Paul, M.J.1    Dredze, M.2
  • 43
    • 84857735176 scopus 로고    scopus 로고
    • Early warning and outbreak detection using social networking websites: The potential of twitter
    • Springer
    • E. de Quincey and P. Kostkova. Early warning and outbreak detection using social networking websites: The potential of twitter. In International Conference on Electronic Healthcare, pages 21–24. Springer, 2009.
    • (2009) International Conference on Electronic Healthcare , pp. 21-24
    • De Quincey, E.1    Kostkova, P.2
  • 44
    • 78349277925 scopus 로고    scopus 로고
    • Monitoring influenza trends through mining social media
    • C. Corley, A.R. Mikler, K.P. Singh, and D.J. Cook. Monitoring influenza trends through mining social media. In BIOCOMP, pages 340–346, 2009.
    • (2009) BIOCOMP , pp. 340-346
    • Corley, C.1    Mikler, A.R.2    Singh, K.P.3    Cook, D.J.4
  • 45
    • 77954764743 scopus 로고    scopus 로고
    • Massive social network analysis: Mining twitter for social good
    • D. Ediger and et al. Massive social network analysis: Mining twitter for social good. In 2010 39th International Conference on Parallel Processing, pages 583–593. IEEE, 2010.
    • (2010) 2010 39th International Conference on Parallel Processing , pp. 583-593
    • Ediger, D.1
  • 47
    • 84862897077 scopus 로고    scopus 로고
    • Web queries as a source for syndromic surveillance
    • A. Hulth, G. Rydevik, and A. Linde. Web queries as a source for syndromic surveillance. PloS ONE, 4(2):e4378, 2009.
    • (2009) PloS ONE , vol.4 , Issue.2
    • Hulth, A.1    Rydevik, G.2    Linde, A.3
  • 48
    • 79955757514 scopus 로고    scopus 로고
    • The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic
    • A. Signorini, A.M. Segre, and P.M. Polgreen. The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic. PloS ONE, 6(5):e19467, 2011.
    • (2011) PloS ONE , vol.6 , Issue.5
    • Signorini, A.1    Segre, A.M.2    Polgreen, P.M.3
  • 51
    • 0022394032 scopus 로고
    • A mathematical model for the global spread of influenza
    • jul
    • L.A. Rvachev and I.M. Longini. A mathematical model for the global spread of influenza. Mathematical Biosciences, 75(1):3–22, jul 1985.
    • (1985) Mathematical Biosciences , vol.75 , Issue.1 , pp. 3-22
    • Rvachev, L.A.1    Longini, I.M.2
  • 52
    • 17544390067 scopus 로고
    • A method for assessing the global spread of HIV-1 infection based on air travel
    • feb
    • A. Flahault and A.J. Valleron. A method for assessing the global spread of HIV-1 infection based on air travel. Mathematical Population Studies, 3(3):161–171, feb 1992.
    • (1992) Mathematical Population Studies , vol.3 , Issue.3 , pp. 161-171
    • Flahault, A.1    Valleron, A.J.2
  • 53
    • 33846670320 scopus 로고    scopus 로고
    • Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
    • V. Colizza and et al. Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions. Plos Med, 4(1):e13, 2007.
    • (2007) Plos Med , vol.4 , Issue.1 , pp. e13
    • Colizza, V.1
  • 55
    • 23644457957 scopus 로고    scopus 로고
    • Containing pandemic influenza at the source
    • I.M. Longini and et al. Containing pandemic influenza at the source. Science, 309:1083–1087, 2005.
    • (2005) Science , vol.309 , pp. 1083-1087
    • Longini, I.M.1
  • 56
    • 67249138142 scopus 로고    scopus 로고
    • Pandemic potential of a strain of influenza A (H1N1): Early findings
    • C. Fraser and et al. Pandemic potential of a strain of influenza A (H1N1): early findings. Science, 324(5934):1557–1561, 2009.
    • (2009) Science , vol.324 , Issue.5934 , pp. 1557-1561
    • Fraser, C.1
  • 57
    • 41349106559 scopus 로고    scopus 로고
    • Time Lines of Infection and Disease in Human Influenza: A Review of Volunteer Challenge Studies
    • C. Fabrice and et al. Time Lines of Infection and Disease in Human Influenza: A Review of Volunteer Challenge Studies. American Journal of Epidemiology, 167(7):775–785, 2008.
    • (2008) American Journal of Epidemiology , vol.167 , Issue.7 , pp. 775-785
    • Fabrice, C.1
  • 58
    • 42149139969 scopus 로고    scopus 로고
    • Seasonal influenza in the United States, France, and Australia: Transmission and prospects for control
    • 6
    • G. Chowell, M.A. Miller, and C. Viboud. Seasonal influenza in the United States, France, and Australia: transmission and prospects for control. Epidemiology & Infection, 136:852–864, 6 2008.
    • (2008) Epidemiology & Infection , vol.136 , pp. 852-864
    • Chowell, G.1    Miller, M.A.2    Viboud, C.3
  • 61
    • 84876053703 scopus 로고    scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • Springer
    • H. Akaike. Information theory and an extension of the maximum likelihood principle. In Selected Papers of Hirotugu Akaike, pages 199–213. Springer, 1998.
    • (1998) Selected Papers of Hirotugu Akaike , pp. 199-213
    • Akaike, H.1
  • 62
    • 84930790662 scopus 로고    scopus 로고
    • Estimating the secondary attack rate and serial interval of influenza-like illnesses using social media
    • E. Yom-Tov, I. Johansson-Cox, V. Lampos, and A. C. Hayward. Estimating the secondary attack rate and serial interval of influenza-like illnesses using social media. Influenza and other respiratory viruses, 9(4):191–199, 2015.
    • (2015) Influenza and Other Respiratory Viruses , vol.9 , Issue.4 , pp. 191-199
    • Yom-Tov, E.1    Johansson-Cox, I.2    Lampos, V.3    Hayward, A.C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.