Forecasting influenza-like illness dynamics for military populations using neural networks and social media
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(Seasonal) WI. Fact Sheet Number 211; 2015. <comment>Available from: <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.who.int/mediacentre/factsheets/fs211/en/" xlink:type="simple">http://www.who.int/mediacentre/factsheets/fs211/en/</ext-link></comment>
Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. In: AMIA Annual Symposium Proceedings. vol. 2006. American Medical Informatics Association; 2006. p. 244.
N Generous, 2014, Global disease monitoring and forecasting with Wikipedia, PLoS Comput Biol, 10, e1003892, 10.1371/journal.pcbi.1003892
DJ McIver, 2014, Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time, PLoS Comput Biol, 10, e1003581, 10.1371/journal.pcbi.1003581
CD Corley, 2010, Text and structural data mining of influenza mentions in web and social media, International journal of environmental research and public health, 7, 596, 10.3390/ijerph7020596
DA Broniatowski, 2013, National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic, PloS one, 8, e83672, 10.1371/journal.pone.0083672
MJ Paul, 2014, Twitter improves influenza forecasting, PLOS Currents Outbreaks
M Santillana, 2015, Combining search, social media, and traditional data sources to improve influenza surveillance, PLoS Comput Biology, 11, e1004513, 10.1371/journal.pcbi.1004513
MJ Paul, 2016, Pacific Symposium on Biocomputing, 21, 468
E Velasco, 2014, Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review, Milbank Quarterly, 92, 7, 10.1111/1468-0009.12038
LE Charles-Smith, 2015, Using social media for actionable disease surveillance and outbreak management: A systematic literature review, PloS one, 10, e0139701, 10.1371/journal.pone.0139701
Smith MC, Broniatowski DA, Paul MJ, Dredze M. Towards Real-Time Measurement of Public Epidemic Awareness: Monitoring Influenza Awareness through Twitter; 2015.
Diaz-Aviles E, Stewart A. Tracking twitter for epidemic intelligence: case study: Ehec/hus outbreak in Germany, 2011. In: Proceedings of the 4th Annual ACM Web Science Conference. ACM; 2012. p. 82–85.
H Feldmann, 2014, Ebola—a growing threat?, New England Journal of Medicine, 371, 1375, 10.1056/NEJMp1405314
Odlum M. How Twitter can support early warning systems in ebola outbreak surveillance. In: 2015 APHA Annual Meeting & Expo (Oct. 31-Nov. 4, 2015). APHA; 2015.
R Chunara, 2012, Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak, The American journal of tropical medicine and hygiene, 86, 39, 10.4269/ajtmh.2012.11-0597
BM Althouse, 2015, Enhancing disease surveillance with novel data streams: challenges and opportunities, EPJ Data Science, 4, 1, 10.1140/epjds/s13688-015-0054-0
EO Nsoesie, 2014, Guess who’s not coming to dinner? Evaluating online restaurant reservations for disease surveillance, Journal of medical Internet research, 16, e22, 10.2196/jmir.2998
CC Freifeld, 2008, HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports, Journal of the American Medical Informatics Association, 15, 150, 10.1197/jamia.M2544
D Das, 2005, Monitoring over-the-counter medication sales for early detection of disease outbreaks—New York City, MMWR Morb Mortal Wkly Rep, 54, 41
Brownstein JS, Mandl KD. Reengineering real time outbreak detection systems for influenza epidemic monitoring. In: AMIA Annual Symposium Proceedings. vol. 2006. American Medical Informatics Association; 2006. p. 866.
DR Olson, 2007, Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City, PLoS Med, 4, e247, 10.1371/journal.pmed.0040247
Coppersmith G, Harman C, Dredze M. Measuring Post Traumatic Stress Disorder in Twitter. In: Proceedings of ICWSM; 2014.
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781. 2013;.
DM Blei, 2003, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, 993
L Deng, 2014, Deep learning: methods and applications, Foundations and Trends, 7, 197
CD Manning, 2008, Scoring, term weighting and the vector space model, Introduction to Information Retrieval, 100, 2
Volkova S, Bell E. Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis. In: Proceedings of the NAACL Workshop on Computational Approaches to Deception Detection; 2016.
Řehůřek R, Sojka P. Software Framework for Topic Modeling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks; 2010. p. 45–50.
Lamb A, Paul MJ, Dredze M. Separating Fact from Fear: Tracking Flu Infections on Twitter. In: Proceedings of HLT-NAACL; 2013. p. 789–795.
P Riley, 2013, Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations, PLoS Comput Biol, 9, e1003064, 10.1371/journal.pcbi.1003064
