Measuring global disease with Wikipedia: Success, failure, and a research agenda

Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Volumn , Issue , 2017, Pages 1812-1834

  Priedhorsky, Reid ^a   Osthus, Dave ^a   Daughton, Ashlynn R ^a   Moran, Kelly R ^a   Generous, Nicholas ^a   Fairchild, Geoffrey ^a   Deshpande, Alina ^a   Del Valle, Sara Y ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

Disease; Epidemiology; Forecasting; Modeling; Wikipedia

Indexed keywords

COMPUTER SUPPORTED COOPERATIVE WORK; DISEASES; EPIDEMIOLOGY; FORECASTING; INTERACTIVE COMPUTER SYSTEMS; MODELS; NETWORK SECURITY; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SEMANTICS;

CONTROLLED EXPERIMENT; DISEASE MONITORING; DISEASE SURVEILLANCE; INFORMATION FLOWS; INTERNET BASED APPROACHES; PUBLIC HEALTH SYSTEMS; SEMANTIC RELATEDNESS; WIKIPEDIA;

GROUPWARE;

EID: 85014791442     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2998181.2998183     Document Type: Conference Paper

References (182)

1. Harshavardhan Achrekar et al. 2011. Predicting flu trends using Twitter data. In Computer Communications Workshops (INFOCOM Workshops)). http://dx.doi.org/10.1109/INFCOMW.2011.5928903
2. Harshavardhan Achrekar et al. 2012. Twitter improves seasonal influenza prediction. In Health Informatics (HEALTHINF). http://www.cs.uml.edu/~bliu/pub/healthinf-2012.pdf
3. Byung Gyu Ahn, Benjamin Van Durme, and Chris Callison-Burch. 2011. WikiTopics: What is popular on Wikipedia and why. In Workshop on Automatic Summarization for Different Genres, Media, and Languages (WASDGML). http://dl.acm.org/citation. cfm?id=2018987.2018992
4. Murray Aitken, Thomas Altmann, and Daniel Rosen. 2014. Engaging patients through social media. Tech report. IMS Institute for Healthcare Informatics.
5. Cristiano Alicino et al. Assessing Ebola-related web search behaviour: Insights and implications from an analytical study of Google Trends-based query volumes. Infectious Diseases of Poverty 4 (2015). http://dx.doi.org/10.1186/s40249-015-0090-9
6. Tim Althoff et al. 2013. Analysis and forecasting of trending topics in online media streams. In Multimedia. http://dx.doi.org/10.1145/2502081.2502117
7. Benjamin M. Althouse, Yih Yng Ng, and Derek A. T. Cummings. Prediction of dengue incidence using search query surveillance. PLOS Neglected Tropical Diseases 5, 8 (Aug. 2011). http://dx.doi.org/10. 1371/journal.pntd.0001258
8. Eiji Aramaki, Sachiko Maskawa, and Mizuki Morita. 2011. Twitter catches the flu: Detecting influenza epidemics using Twitter. In Empirical Methods in Natural Language Processing (EMNLP). http://dl. acm.org/citation. cfm?id=2145432.2145600
9. Ozgur M. Araz, Dan Bentley, and Robert L. Muelleman. Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska. The American Journal of Emergency Medicine 32, 9 (Sept. 2014). http://dx.doi.org/10. 1016/j.ajem.2014.05.052
10. Anoshé A. Aslam et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. Journal of Medical Internet Research 16, 11 (Nov. 2014). http://dx.doi.org/10.2196/jmir.3532
11. John W. Ayers et al. Seasonality in seeking mental health information on Google. American Journal of Preventive Medicine 44, 5 (May 2013). http://dx. doi.org/10.1016/j.amepre.2013.01.012
12. Gyung Jin Bahk, Yong Soo Kim, and Myoung Su Park. Use of internet search queries to enhance surveillance of foodborne illness. Emerging Infectious Diseases 21, 11 (Nov. 2015). http://dx.doi.org/10.3201/eid2111.141834
13. Batuhan Bardak and Mehmet Tan. 2015. Prediction of influenza outbreaks by integrating Wikipedia article access logs and Google Flu Trend data. In IEEE Bioinformatics and Bioengineering (BIBE). http://dx.doi.org/10.1109/BIBE.2015.7367640
14. Michal Bogdziewicz and Jakub Szymkowiak. Oak acorn crop and Google search volume predict Lyme disease risk in temperate Europe. Basic and Applied Ecology (Jan. 2016). http://dx.doi.org/10.1016/j.baae.2016.01.002
15. Stephanie M. Borchardt, Kathleen A. Ritger, and Mark S. Dworkin. Categorization, prioritization, and surveillance of potential bioterrorism agents. Infectious Disease Clinics of North America 20, 2 (June 2006). http://dx.doi.org/10.1016/j.idc.2006.02. 005
16. Dena M. Bravata et al. Systematic review: Surveillance systems for early detection of bioterrorism-related diseases. Annals of Internal Medicine 140, 11 (June 2004). http://dx.doi.org/10.7326/0003-4819-140-11-200406010-00013
17. Benjamin N. Breyer et al. Use of Google Insights for Search to track seasonal and geographic kidney stone incidence in the United States. Urology 78, 2 (Aug. 2011). http://dx.doi.org/10.1016/j.urology. 2011.01.010
18. Francesco Brigo and Roberto Erro. Why do people Google movement disorders? An infodemiological study of information seeking behaviors. Neurological Sciences (Feb. 2016). http://dx.doi.org/10.1007/s10072-016-2501-5
19. David Andre Broniatowski et al. Using social media to perform local influenza surveillance in an inner-city hospital: A retrospective observational study. JMIR Public Health and Surveillance 1, 1 (2015). http://dx.doi.org/10.2196/publichealth.4472
20. David A. Broniatowski, Michael J. Paul, and Mark Dredze. National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic. PLOS ONE 8, 12 (Dec. 2013). http://dx. doi.org/10.1371/journal.pone.0083672
21. Logan C. Brooks et al. Flexible modeling of epidemics with an empirical bayes framework. PLOS Computational Biology 11, 8 (Aug. 2015). http://dx. doi.org/10.1371/journal.pcbi.1004382
22. Matt Brooks. Was the NBA draft lottery rigged for the New Orleans Hornets to win? Washington Post (May 2012). https://www.washingtonpost.com/blogs/early-lead/post/was-the-nba-draft-lotteryrigged-for-the-new-orleans-hornets-towin/2012/05/31/gJQAmL5V4U-blog.html
23. Jan Burdziej and Piotr Gawrysiak. 2012. Using web mining for discovering spatial patterns and hot spots for spatial generalization. In Foundations of Intelligent Systems, Li Chen et al. (Eds.). Number 7661. http://link.springer.com/chapter/10.1007/978-3-642-34624-8-21
24. Declan Butler. When Google got flu wrong. Nature 494, 7436 (Feb. 2013). http://dx.doi.org/10.1038/494155a
25. Herman Anthony Carneiro and Eleftherios Mylonakis. Google Trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases 49, 10 (Nov. 2009). http://dx.doi.org/10.1086/630200
26. Rachael Cayce, Kathleen Hesterman, and Paul Bergstresser. Google technology in the surveillance of hand foot mouth disease in Asia. International Journal of Integrative Pediatrics and Environmental Medicine 1 (2014). http://www.ijipem.com/index.php/ijipem/article/view/6
27. Centers for Disease Control and Prevention (CDC). MMWR morbidity tables. (2015). http://wonder.cdc.gov/mmwr/mmwrmorb.asp
28. 2016. Overview of influenza surveillance in the United States. Technical Report. Centers for Disease Control and Prevention (CDC). http://www.cdc.gov/flu/pdf/weekly/overview.pdf
29. Boris Cergol and Matjaž Omladic. What can Wikipedia and Google tell us about stock prices under diferent market regimes? Ars Mathematica Contemporanea 9, 2 (June 2015). http://amcjournal.eu/index.php/amc/article/view/561
30. Prithwish Chakraborty et al. 2014. Forecasting a moving target: Ensemble models for ILI case count predictions. In SIAM Data Mining. http://epubs.siam.org/doi/abs/10.1137/1. 9781611973440.30
31. Emily H. Chan et al. Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLOS Neglected Tropical Diseases 5, 5 (May 2011). http://dx.doi.org/10. 1371/journal.pntd.0001206
32. Jedsada Chartree. 2014. Monitoring dengue outbreaks using online data. Ph. D. University of North Texas. http://digital.library.unt.edu/ark:/67531/metadc500167/m2/1/high-res-d/dissertation.pdf
33. Sungjin Cho et al. Correlation between national influenza surveillance data and Google Trends in South Korea. PLOS ONE 8, 12 (Dec. 2013). http://dx.doi.org/10.1371/journal.pone.0081422
34. Rumi Chunara et al. Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010-2011. Malaria Journal 11, 1 (Feb. 2012). http://dx.doi.org/10.1186/1475-2875-11-43
35. Rumi Chunara et al. Flu Near You: An online self-reported influenza surveillance system in the USA. Online Journal of Public Health Informatics 5, 1 (March 2013). http://dx.doi.org/10.5210/ojphi.v5i1.4456
36. Rumi Chunara, Jason R Andrews, and John S Brownstein. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. American Journal of Tropical Medicine and Hygiene 86, 1 (Jan. 2012). http://dx.doi.org/10.4269/ajtmh.2012.11-0597
37. Marek Ciglan and Kjetil Nørvåg. 2010. WikiPop: Personalized event detection system based on Wikipedia page view statistics. In Information and Knowledge Management (CIKM). http://dx.doi.org/10.1145/1871437.1871769
38. Nigel Collier et al. BioCaster: Detecting public health rumors with a Web-based text mining system. Bioinformatics 24, 24 (Dec. 2008). http://dx.doi.org/10.1093/bioinformatics/btn534
39. Crystale Purvis Cooper et al. Cancer internet search activity on a major search engine, United States 2001-2003. Journal of Medical Internet Research 7, 3 (July 2005). http://dx.doi.org/10.2196/jmir.7.3.e36
40. Aron Culotta. 2010. Towards detecting influenza epidemics by analyzing Twitter messages. In Workshop on Social Media Analytics (SOMA). http://dx.doi.org/10.1145/1964858.1964874
41. Aron Culotta. Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. Language Resources and Evaluation 47, 1 (March 2013). http://dx.doi.org/10.1007/s10579-012-9185-0
42. Aron Culotta. 2014. Estimating county health statistics with Twitter. In Human Factors in Computing Systems (CHI). http://dx.doi.org/10.1145/2556288.2557139
43. Michael W. Davidson, Dotan A. Haim, and Jennifer M. Radin. Using networks to combine "big data" and traditional surveillance to improve influenza predictions. Scientific Reports 5 (Jan. 2015). http://dx.doi.org/10.1038/srep08154
44. Brian de Silva and Ryan Compton. Prediction of foreign box office revenues based on Wikipedia page activity. arXiv:1405.5924 [cs. SI] (May 2014). http://arxiv.org/abs/1405.5924
45. Rishi Desai et al. Norovirus disease surveillance using Google internet query share data. Clinical Infectious Diseases 55, 8 (Oct. 2012). http://dx.doi.org/10.1093/cid/cis579
46. Son Doan, Lucila Ohno-Machado, and Nigel Collier. 2012. Enhancing Twitter data analysis with simple semantic filtering: Example in tracking influenza-like illnesses. In Healthcare Informatics, Imaging and Systems Biology (HISB). http://dx.doi.org/10.1109/HISB.2012.21
47. Timothy J. Doyle, M. Kathleen Glynn, and Samuel L. Groseclose. Completeness of notifiable infectious disease reporting in the United States: An analytical literature review. American Journal of Epidemiology 155, 9 (Jan. 2002). http://dx.doi.org/10.1093/aje/155.9.866
48. Andrea Freyer Dugas et al. Influenza forecasting with Google Flu Trends. PLOS ONE 8, 2 (Feb. 2013). http://dx.doi.org/10.1371/journal.pone. 0056176
49. Vanja M. Dukic, Michael Z. David, and Diane S. Lauderdale. Internet queries and methicillin-resistant Staphylococcus aureus surveillance. Emerging Infectious Diseases 17, 6 (June 2011). http://dx.doi.org/10.3201/eid1706.101451
50. Michael Edelstein et al. Detecting the norovirus season in Sweden using search engine data - Meeting the needs of hospital infection control teams. PLOS ONE 9, 6 (June 2014). http://dx.doi.org/10.1371/journal.pone.0100309
51. Johannes C. Eichstaedt et al. Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science 26, 2 (Feb. 2015). http://dx.doi.org/10.1177/0956797614557867
52. Andreas Ekström et al. Forecasting emergency department visits using internet data. Annals of Emergency Medicine 65, 4 (April 2015). http://dx. doi.org/10.1016/j.annemergmed.2014.10.008
53. Gunther Eysenbach. Infodemiology: Tracking flu-related searches on the web for syndromic surveillance. AMIA Annual Symposium 2006 (2006). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839505
54. Geoffrey Fairchild et al. 2015. Eliciting disease data from Wikipedia articles. In Weblogs and Social Media (ICWSM) Workshops. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10630
55. Clark C. Freifeld et al. HealthMap: Global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association 15, 2 (Jan. 2008). http://dx.doi.org/10.1197/jamia. M2544
56. Thomas R. Frieden. A framework for public health action: The health impact pyramid. American Journal of Public Health 100, 4 (April 2010). http://dx.doi.org/10.2105/AJPH.2009.185652
57. Nicholas Generous et al. Global disease monitoring and forecasting with Wikipedia. PLOS Computational Biology 10, 11 (Nov. 2014). http://dx.doi.org/10. 1371/journal.pcbi.1003892
58. Francesco Gesualdo et al. Can Twitter be a source of information on allergy? Correlation of pollen counts with tweets reporting symptoms of allergic rhinoconjunctivitis and names of antihistamine drugs. PLOS ONE 10, 7 (July 2015). http://dx.doi.org/10.1371/journal.pone.0133706
59. Jeremy Ginsberg et al. Detecting influenza epidemics using search engine query data. Nature 457, 7232 (Nov. 2008). http://dx.doi.org/10.1038/nature07634
60. Steven Gittelman et al. A new source of data for public health surveillance: Facebook likes. Journal of Medical Internet Research 17, 4 (April 2015). http://dx.doi.org/10.2196/jmir.3970
61. Sharad Goel et al. Predicting consumer behavior with Web search. PNAS 107, 41 (Oct. 2010). http://dx.doi.org/10.1073/pnas.1005962107
62. Janaína Gomide et al. 2011. Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. In Web Science Conference (WebSci). http://www.websci11.org/fileadmin/websci/Papers/92-paper.pdf
63. Yuzhou Gu et al. Early detection of an epidemic erythromelalgia outbreak using Baidu search data. Scientific Reports 5 (July 2015). http://dx.doi.org/10.1038/srep12649
64. Akihito Hagihara, Shogo Miyazaki, and Takeru Abe. Internet suicide searches and the incidence of suicide in young people in Japan. European Archives of Psychiatry and Clinical Neuroscience 262, 1 (Feb. 2012). http://dx.doi.org/10.1007/s00406-011-0212-8
65. Francis H. Harlow and Jacob E. Fromm. Computer experiments in fluid dynamics. Scientific American 212, 3 (March 1965).
66. Miguel Helft. Google uses web searches to track flu's spread. The New York Times (Nov. 2008). http://www.nytimes.com/2008/11/12/technology/internet/12flu.html
67. Kyle S. Hickmann et al. Forecasting the 2013-2014 influenza season using Wikipedia. PLOS Computational Biology 11, 5 (May 2015). http://dx. doi.org/10.1371/journal.pcbi.1004239
68. Hideo Hirose and Liangliang Wang. 2012. Prediction of infectious disease spread using Twitter: A case of influenza. In Parallel Architectures, Algorithms and Programming (PAAP). http://dx.doi.org/10.1109/PAAP.2012.23
69. Arthur E. Hoerl and Robert W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 1 (Feb. 1970). http://dx.doi.org/10.1080/00401706.1970.10488634
70. Martin Rudi Holaker and Eirik Emanuelsen. 2013. Event detection using Wikipedia. Master's thesis. Institutt for Datateknikk og Informasjonsvitenskap. http://www.diva-portal.org/smash/record. jsf?pid=diva2:655606
71. Anette Hulth et al. Eye-opening approach to norovirus surveillance. Emerging Infectious Diseases 16, 8 (Aug. 2010). http://dx.doi.org/10.3201/eid1608.100093
72. Anette Hulth and Gustaf Rydevik. Web query-based surveillance in Sweden during the influenza A (H1N1) 2009 pandemic, April 2009 to February 2010. Euro Surveillance 16, 18 (2011).
73. Anette Hulth, Gustaf Rydevik, and Annika Linde. Web queries as a source for syndromic surveillance. PLOS ONE 4, 2 (Feb. 2009). http://dx.doi.org/10. 1371/journal.pone.0004378
74. Robert Koch Institute. SurvStat@RKI 2.0. (2016). https://survstat.rki.de/Content/Query/Create.aspx
75. Instituto Nacional de Salud. Boletín epidemiológico. (2015). http://www.ins.gov. co/boletinepidemiologico/Paginas/default.aspx
76. Molly E. Ireland et al. Action tweets linked to reduced county-level HIV prevalence in the United States: Online messages and structural determinants. AIDS and Behavior (Dec. 2015). http://dx.doi.org/10.1007/s10461-015-1252-2
77. Bao Jia-xing et al. 2013. Gonorrhea incidence forecasting research based on Baidu search data. In Management Science and Engineering (ICMSE). http://dx.doi.org/10.1109/ICMSE.2013. 6586259
78. Amy K. Johnson and Supriya D. Mehta. A comparison of internet search trends and sexually transmitted infection rates using Google Trends. Sexually Transmitted Diseases 41, 1 (Jan. 2014). http://dx. doi.org/10.1097/OLQ.0000000000000065
79. Heather A Johnson et al. Analysis of Web access logs for surveillance of influenza. Studies in Health Technology and Informatics 107, 2 (2004). http://www.ncbi.nlm.nih.gov/pubmed/15361003
80. Mirko Kämpf et al. The detection of emerging trends using Wikipedia traffic data and context networks. PLOS ONE 10, 12 (Dec. 2015). http://dx.doi.org/10.1371/journal.pone.0141892
81. Min Kang et al. Using Google Trends for influenza surveillance in South China. PLOS ONE 8, 1 (Jan. 2013). http://dx.doi.org/10.1371/journal. pone.0055205
82. M.-G. Kang et al. Google unveils a glimpse of allergic rhinitis in the real world. Allergy 70, 1 (Jan. 2015). http://dx.doi.org/10.1111/all.12528
83. Asad Ullah Rafiq Khan, Mohammad Badruddin Khan, and Khalid Mahmood. 2015. Cloud service for assessment of news' popularity in internet based on Google and Wikipedia indicators. In National Symposium on Information Technology: Towards New Smart World (NSITNSW). http://dx.doi.org/10. 1109/NSITNSW.2015.7176417
84. Eui-Ki Kim et al. Use of Hangeul Twitter to track and predict human influenza infection. PLOS ONE 8, 7 (July 2013). http://dx.doi.org/10.1371/journal. pone.0069305
85. Kwang Deok Kim and Liaquat Hossain. 2014. Towards early detection of influenza epidemics by using social media analytics. In DSS 2.0 - Supporting Decision Making with New Technologies. Vol. 261. http://dx.doi.org/10.3233/978-1-61499-399-5-36
86. Nicholas E. Kman and Daniel J. Bachmann. Biosurveillance: a review and update. Advances in Preventive Medicine 2012 (Jan. 2012). http://dx.doi.org/10.1155/2012/301408
87. Volker König and Ralph Mösges. A model for the determination of pollen count using Google search queries for patients suffering from allergic rhinitis. Journal of Allergy 2014 (June 2014). http://dx.doi.org/10.1155/2014/381983
88. Natalie Kupferberg and Bridget McCrate Protus. Accuracy and completeness of drug information in Wikipedia: An assessment. Journal of the Medical Library Association 99, 4 (Oct. 2011). http://dx.doi.org/10.3163/1536-5050.99.4.010
89. Alex Lamb, Michael J. Paul, and Mark Dredze. 2013. Separating fact from fear: Tracking flu infections on Twitter. In Human Language Technologies (NAACL-HLT). http://www.aclweb.org/anthology/N/N13/N13-1097.pdf
90. Vasileios Lampos et al. Advances in nowcasting influenza-like illness rates using search query logs. Scientific Reports 5 (Aug. 2015). http://dx.doi.org/10.1038/srep12760
91. Vasileios Lampos et al. Assessing the impact of a health intervention via user-generated Internet content. Data Mining and Knowledge Discovery 29, 5 (July 2015). http://dx.doi.org/10.1007/s10618-015-0427-9
92. Vasileios Lampos and Nello Cristianini. 2010. Tracking the flu pandemic by monitoring the social web. In Cognitive Information Processing (CIP). http://dx.doi.org/10.1109/CIP.2010.5604088
93. Vasileios Lampos and Nello Cristianini. Nowcasting events from the social web with statistical learning. Transactions on Intelligent Systems and Technology 3, 4 (Sept. 2012). http://dx.doi.org/10.1145/2337542.2337557
94. Michaël R. Laurent and Tim J. Vickers. Seeking Health Information Online: Does Wikipedia Matter? Journal of the American Medical Informatics Association 16, 4 (July 2009). http://dx.doi.org/10.1197/jamia. M3059
95. David Lazer et al. The parable of Google Flu: Traps in big data analysis. Science 343, 14 March (2014).
96. Andreas Leithner et al. Wikipedia and osteosarcoma: A trustworthy patients' information? Journal of the American Medical Informatics Association 17, 4 (Jan. 2010). http://dx.doi.org/10.1136/jamia.2010. 004507
97. Shengli Li and Xichuan Zhou. Research of the correlation between the H1N1 morbidity data and Google Trends in Egypt. arXiv:1511.05300 [cs. SI] (Nov. 2015). http://arxiv.org/abs/1511.05300
98. Johan Lindh et al. Head lice surveillance on a deregulated OTC-sales market: A study using web query data. PLOS ONE 7, 11 (Nov. 2012). http://dx. doi.org/10.1371/journal.pone.0048666
99. Ruoqian Liu et al. 2014. Enhancing financial decision-making using social behavior modeling. In Social Network Mining and Analysis (SNAKDD). http://dx.doi.org/10.1145/2659480.2659505
100. Kevin Lutsky, Joseph Bernstein, and Pedro Beredjiklian. Quality of information on the internet about carpal tunnel syndrome: An update. Orthopedics 36, 8 (2013). http://www.healio.com/orthopedics/journals/ortho/%7Bf97c8407-7483-4d26-9aac-2b860b0e6d2c%7D/quality-of-information-onthe-internet-about-carpal-tunnel-syndromean-update
101. T. Ma et al. Syndromic surveillance of influenza activity in Sweden: an evaluation of three tools. Epidemiology & Infection 143, 11 (Aug. 2015). http://dx.doi.org/10.1017/S0950268814003240
102. Douglas Martin. Jack Twyman, N. B. A. star, dies at 78. The New York Times (May 2012). http://www.nytimes.com/2012/06/01/sports/basketball/jack-twyman-nba-star-dies-at-78.html
103. Leah J. Martin, B. E. Lee, and Yutaka Yasui. Google Flu Trends in Canada: A comparison of digital disease surveillance data with physician consultations and respiratory virus surveillance data, 2010-2014. Epidemiology & Infection 144, 02 (Jan. 2016). http://dx.doi.org/10.1017/S0950268815001478
104. Leah J. Martin, Biying Xu, and Yutaka Yasui. Improving Google Flu Trends estimates for the United States through transformation. PLOS ONE 9, 12 (Dec. 2014). http://dx.doi.org/10.1371/journal. pone.0109209
105. David J. McIver and John S. Brownstein. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLOS Computational Biology 10, 4 (April 2014). http://dx. doi.org/10.1371/journal.pcbi.1003581
106. Wes McKinney. 2010. Data structures for statistical computing in Python. In Python in Science (SCIPY), Vol. 445. http://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf
107. Anthony J. McMichael. Globalization, climate change, and human health. New England Journal of Medicine 368, 14 (April 2013). http://dx.doi.org/10.1056/NEJMra1109341
108. Márton Mestyán, Taha Yasseri, and János Kertész. Early prediction of movie box office success based on Wikipedia activity big data. PLOS ONE 8, 8 (Aug. 2013). http://dx.doi.org/10.1371/journal. pone.0071226
109. Gabriel J. Milinovich et al. Using internet search queries for infectious disease surveillance: Screening diseases for suitability. BMC Infectious Diseases 14 (2014). http://dx.doi.org/10.1186/s12879-014-0690-1
110. David Milne and Ian H. Witten. An open-source toolkit for mining Wikipedia. Artificial Intelligence 194 (Jan. 2013). http://dx.doi.org/10.1016/j.artint. 2012.06.007
111. Ministry of Health Israel. Weekly epidemiological reports. (2015). http://www.health.gov. il/UnitsOffice/HD/PH/epidemiology/Pages/epidemiology-report.aspx
112. Susan M. Mniszewski et al. 2014. Understanding the impact of face mask usage through epidemic simulation of large social networks. In Theories and Simulations of Complex Social Systems, Vahid Dabbaghian and Vijay Kumar Mago (Eds.). Number 52. http://link.springer.com/chapter/10.1007/978-3-642-39149-1-8
113. Helen Susannah Moat et al. Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports 3 (May 2013). http://dx.doi.org/10.1038/srep01801
114. Helen Susannah Moat et al. 2014. Anticipating stock market movements with Google and Wikipedia. In Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale, Davron Matrasulov and H. Eugene Stanley (Eds.). http://dx.doi.org/10.1007/978-94-017-8704-8-4
115. Ruchit Nagar et al. A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. Journal of Medical Internet Research 16, 10 (Oct. 2014). http://dx.doi.org/10.2196/jmir.3416
116. Anna C. Nagel et al. The complex relationship of realspace events and messages in cyberspace: Case study of influenza and pertussis using tweets. Journal of Medical Internet Research 15, 10 (Oct. 2013). http://dx.doi.org/10.2196/jmir.2705
117. N. J. D. Nagelkerke. A note on a general definition of the coefficient of determination. Biometrika 78, 3 (1991). http://dx.doi.org/10.2307/2337038
118. Kok W. Ng. 2014. The use of Twitter to predict the level of influenza activity in the United States. M. S. Naval Postgraduate School. http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html& identifier=ADA620696
119. Alex J. Ocampo, Rumi Chunara, and John S. Brownstein. Using search queries for malaria surveillance, Thailand. Malaria Journal 12, 1 (Nov. 2013). http://dx.doi.org/10.1186/1475-2875-12-390
120. Donald R. Olson et al. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: A comparative epidemiological study at three geographic scales. PLOS Computational Biology 9, 10 (Oct. 2013). http://dx.doi.org/10.1371/journal.pcbi.1003256
121. Miles Osborne et al. 2012. Bieber no more: First story detection using Twitter and Wikipedia. In SIGIR Workshop on Time-aware Information Access (TAIA). http://www.dcs.gla.ac.uk/~craigm/publications/osborneTAIA2012.pdf
122. John Paparrizos, Ryen W. White, and Eric Horvitz. Screening for pancreatic adenocarcinoma using signals from web search logs: Feasibility study and results. Journal of Oncology Practice (June 2016). http://dx.doi.org/10.1200/JOP.2015.010504
123. Michael J. Paul and Mark Dredze. 2011. You are what you tweet: Analyzing Twitter for public health. In Weblogs and Social Media (ICWSM).
124. Michael J. Paul, Mark Dredze, and David Broniatowski. Twitter improves influenza forecasting. PLOS Currents (Oct. 2014). http://dx.doi.org/10.1371/currents. outbreaks.90b9ed0f59bae4ccaa683a39865d9117
125. Fabian Pedregosa et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, Oct. (2011). http://jmlr.org/papers/v12/pedregosa11a.html
126. Camille Pelat et al. More diseases tracked by using Google Trends. Emerging Infectious Diseases 15, 8 (Aug. 2009). http://dx.doi.org/10.3201/eid1508.090299
127. Geng Peng and Jiyuan Wang. 2014. Detecting syphilis amount in China based on Baidu query data. In Soft Computing in Information Communication Technology (SCICT 2014). http://dx.doi.org/10.2991/scict-14.2014.43
128. Fernando Pérez and Brian E. Granger. IPython: A system for interactive scientific computing. Computing in Science & Engineering 9, 3 (2007). http://dx.doi.org/10.1109/MCSE.2007.53
129. Lyle R. Petersen et al. Zika virus. New England Journal of Medicine 374, 16 (April 2016). http://dx.doi.org/10.1056/NEJMra1602113
130. David T. Plante and David G. Ingram. Seasonal trends in tinnitus symptomatology: Evidence from Internet search engine query data. European Archives of Oto-Rhino-Laryngology 272, 10 (Sept. 2014). http://dx.doi.org/10.1007/s00405-014-3287-9
131. Philip M. Polgreen et al. Using internet searches for influenza surveillance. Clinical Infectious Diseases 47, 11 (Jan. 2008). http://dx.doi.org/10.1086/593098
132. Tobias Preis and Helen Susannah Moat. Adaptive nowcasting of influenza outbreaks using Google searches. Royal Society Open Science 1, 2 (Oct. 2014). http://dx.doi.org/10.1098/rsos.140095
133. Reid Priedhorsky et al. 2007. Creating, destroying, and restoring value in Wikipedia. In Supporting Group Work (GROUP). http://dx.doi.org/10.1145/1316624.1316663
134. Reid Priedhorsky, Geoffrey Fairchild, and Sara Y. Del Valle. Research:Geo-aggregation of Wikipedia pageviews. (2015). https://meta.wikimedia.org/wiki/Research:Geoaggregation-of-Wikipedia-pageviews
135. Malolan S. Rajagopalan et al. Patient-oriented cancer information on the internet: A comparison of Wikipedia and a professionally maintained database. Journal of Oncology Practice 7, 5 (Jan. 2011). http://dx.doi.org/10.1200/JOP.2010.000209
136. Sudha Ram et al. Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical and Health Informatics 19, 4 (July 2015). http://dx.doi.org/10.1109/JBHI.2015. 2404829
137. Ronald E. Rice. Influences, usage, and outcomes of Internet health information searching: Multivariate results from the Pew surveys. International Journal of Medical Informatics 75, 1 (Jan. 2006). http://dx. doi.org/10.1016/j.ijmedinf.2005.07.032
138. Joshua Ritterman, Miles Osborne, and Ewan Klein. 2009. Using prediction markets and Twitter to predict a swine flu pandemic. In Workshop on Mining Social Media. http://homepages.inf.ed.ac.uk/miles/papers/swine09.pdf
139. Caitlin M. Rivers et al. Modeling the impact of interventions on an epidemic of Ebola in Sierra Leone and Liberia. PLOS Currents (2014). http://dx.doi.org/10.1371/currents. outbreaks.4d41fe5d6c05e9df30ddce33c66d084c
140. Ankit Rohatgi. WebPlotDigitizer. (Oct. 2015). http://arohatgi.info/WebPlotDigitizer
141. Mauricio Santillana et al. Using clinicians' search query data to monitor influenza epidemics. Clinical Infectious Diseases 59, 10 (Nov. 2014). http://dx.doi.org/10.1093/cid/ciu647
142. Mauricio Santillana et al. What can digital disease detection learn from (an external revision to) Google Flu Trends? American Journal of Preventive Medicine 47, 3 (Sept. 2014). http://dx.doi.org/10.1016/j. amepre.2014.05.020
143. Mauricio Santillana et al. Combining search, social media, and traditional data sources to improve influenza surveillance. PLOS Computational Biology 11, 10 (Oct. 2015). http://dx.doi.org/10.1371/journal. pcbi.1004513
144. Sercan Sarigul and Huaxia Rui. 2014. Nowcasting obesity in the U. S. using Google search volume data. In AAEA/EAAE/CAES Joint Symposium: Social Networks, Social Media and the Economics of Food. http://econpapers.repec.org/paper/agsaajs14/166113.htm
145. Shilad Sen et al. 2014. WikiBrain: Democratizing computation on Wikipedia. In OpenSym. http://dx.doi.org/10.1145/2641580.2641615
146. Dong-Woo Seo et al. Cumulative query method for influenza surveillance using search engine data. Journal of Medical Internet Research 16, 12 (Dec. 2014). http://dx.doi.org/10.2196/jmir.3680
147. Jeffrey Shaman and Alicia Karspeck. Forecasting seasonal outbreaks of influenza. Proceedings of the National Academy of Sciences 109, 50 (Nov. 2012). http://dx.doi.org/10.1073/pnas.1208772109
148. Alessio Signorini. 2014. Use of social media to monitor and predict outbreaks and public opinion on health topics. Ph. D. University of Iowa. http://ir.uiowa.edu/etd/1503
149. Alessio Signorini, Alberto Maria Segre, and Philip M. Polgreen. The use of Twitter to track levels of disease activity and public concern in the U. S. during the influenza A H1N1 pandemic. PLOS ONE 6, 5 (May 2011). http://dx.doi.org/10.1371/journal. pone.0019467
150. Amit Singhal. Introducing the Knowledge Graph: Things, not strings. (May 2012). https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-thingsnot.html
151. Giovanni Stilo et al. 2014. Predicting flu epidemics using Twitter and historical data. In Brain Informatics and Health, Dominik Slezak et al. (Eds.). Number 8609. http://dx.doi.org/10.1007/978-3-319-09891-3-16
152. Michael Strube and Simone Paolo Ponzetto. 2006. WikiRelate! Computing semantic relatedness using Wikipedia. In AAAI, Vol. 6. http://www.aaai.org/Papers/AAAI/2006/AAAI06-223.pdf
153. Yla Tausczik et al. Public Anxiety and Information Seeking Following the H1N1 Outbreak: Blogs, Newspaper Articles, and Wikipedia Visits. Health Communication 27, 2 (2012). http://dx.doi.org/10.1080/10410236.2011.571759
154. Flu Trends Team. The next chapter for Flu Trends. (Aug. 2015). http://googleresearch.blogspot.com/2015/08/the-next-chapter-for-flu-trends.html
155. Marijn ten Thij et al. Modeling page-view dynamics on Wikipedia. arXiv:1212.5943 [physics] (Dec. 2012). http://arxiv.org/abs/1212.5943
156. Garry R. Thomas et al. An evaluation of Wikipedia as a resource for patient education in nephrology. Seminars in Dialysis 26, 2 (2013). http://dx.doi.org/10.1111/sdi.12059
157. L. H. Thompson et al. Emergency department and 'Google Flu Trends' data as syndromic surveillance indicators for seasonal influenza. Epidemiology & Infection 142, 11 (Nov. 2014). http://dx.doi.org/10.1017/S0950268813003464
158. Anna R. Thorner et al. Correlation between UpToDate searches and reported cases of Middle East respiratory syndrome during outbreaks in Saudi Arabia. Open Forum Infectious Diseases 3, 1 (Jan. 2016). http://dx.doi.org/10.1093/ofid/ofw043
159. Robert Tibshirani. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B 58, 1 (Jan. 1996). http://dx.doi.org/10.2307/2346178
160. Ramine Tinati, Thanassis Tiropanis, and Lesie Carr. 2013. An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries. In World Wide Web (WWW) Companion. http://dl.acm.org/citation. cfm?id=2487788.2488177
161. Khoi-Nguyen Tran and Peter Christen. 2013. Cross language prediction of vandalism on Wikipedia using article views and revisions. In Advances in Knowledge Discovery and Data Mining, Jian Pei et al. (Eds.). Number 7819. http://link.springer.com/chapter/10.1007/978-3-642-37456-2-23
162. Brian P. Walcott et al. Determination of geographic variance in stroke prevalence using Internet search engine analytics. Journal of Neurosurgery 115, 6 (June 2011). http://dx.doi.org/10.3171/2011.2. FOCUS1124
163. Zheng Wang et al. 2015. Dynamic Poisson autoregression for influenza-like-illness case count prediction. In ACM Knowledge Discovery and Data Mining (KDD). http://dx.doi.org/10.1145/2783258.2783291
164. Pengyu Wei and Ning Wang. 2016. Wikipedia and stock return: Wikipedia usage pattern helps to predict the individual stock movement. In World Wide Web (WWW) Companion. http://dx.doi.org/10.1145/2872518.2890089
165. Thomas J. Willson et al. Correlating regional aeroallergen effects on internet search activity. Otolaryngology - Head and Neck Surgery 152, 2 (Feb. 2015). http://dx.doi.org/10.1177/0194599814560149
166. Kumanan Wilson and John S. Brownstein. Early detection of disease outbreaks using the Internet. Canadian Medical Association Journal 180, 8 (April 2009). http://dx.doi.org/10.1503/cmaj.1090215
167. Danqing Xu et al. 2011. Predicting epidemic tendency through search behavior analysis. In International Joint Conference on Artificial Intelligence (IJCAI). http://dx.doi.org/10.5591/978-1-57735-516-8/IJCAI11-393
168. Wei Xu, Zhen-Wen Han, and Jian Ma. 2010. A neural netwok based approach to detect influenza epidemics using search engine query data. In Machine Learning and Cybernetics (ICMLC), Vol. 3. http://dx.doi.org/10.1109/ICMLC.2010.5580851
169. Albert C. Yang et al. Association of Internet search trends with suicide death in Taipei City, Taiwan, 2004-2009. Journal of Affective Disorders 132, 1-2 (July 2011). http://dx.doi.org/10.1016/j.jad. 2011.01.019
170. Wan Yang, Marc Lipsitch, and Jeffrey Shaman. Inference of seasonal and pandemic influenza transmission dynamics. Proceedings of the National Academy of Sciences 112, 9 (March 2015). http://dx.doi.org/10.1073/pnas.1415012112
171. Taha Yasseri and Jonathan Bright. Can electoral popularity be predicted using socially generated big data? arXiv:1312.2818 [physics] (Dec. 2013). http://arxiv.org/abs/1312.2818
172. Taha Yasseri and Jonathan Bright. Wikipedia traffic data and electoral prediction: Towards theoretically informed models. arXiv:1505.01818 [cs. SI] (Jan. 2016). http://arxiv.org/abs/1505.01818
173. Qingyu Yuan et al. Monitoring influenza epidemics in China with search query from Baidu. PLOS ONE 8, 5 (May 2013). http://dx.doi.org/10.1371/journal.pone.0064323
174. Erik Zachte. Wikimedia traffic analysis report - Page views per Wikipedia language - Breakdown. (Feb. 2016). https://stats.wikimedia.org/archive/squid-reports/2016-01/SquidReportPageViewsPerLanguageBreakdown.htm
175. Erik Zachte. Wikimedia traffic analysis report - Wikipedia page views per country - Breakdown. (Feb. 2016). https://stats.wikimedia.org/archive/squid-reports/2016-01/SquidReportPageViewsPerCountryBreakdown.htm
176. Wenli Zhang et al. 2016. Extracting signals from social media for chronic disease surveillance. In ACM Digital Health (DH). http://dx.doi.org/10.1145/2896338.2897728
177. Andrey Zheluk et al. Internet search patterns of human immunodeficiency virus and the digital divide in the Russian Federation: Infoveillance study. Journal of Medical Internet Research 15, 11 (Nov. 2013). http://dx.doi.org/10.2196/jmir.2936
178. Xichuan Zhou et al. Monitoring epidemic alert levels by analyzing internet search volume. IEEE Transactions on Biomedical Engineering 60, 2 (2013). http://dx.doi.org/10.1109/TBME.2012.2228264
179. Xichuan Zhou et al. Assessing Google Correlate queries for influenza H1N1 surveillance in Asian developing countries. arXiv:1512.03132 [cs. CY] (Dec. 2015). http://arxiv.org/abs/1512.03132
180. Xi-chuan Zhou and Hai-bin Shen. Notifiable infectious disease surveillance with data collected by search engine. Journal of Zhejiang University SCIENCE C 11, 4 (April 2010). http://dx.doi.org/10.1631/jzus. C0910371
181. Hui Zou and Trevor Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B 67, 2 (2005). http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x
182. Yuanqiang Zou et al. 2015. Prediction of influenza epidemics at the province level in China using search query from "Haosou". In IEEE Fuzzy Systems and Knowledge Discovery (FSKD). http://dx.doi.org/10.1109/FSKD.2015.7382157