1932

Abstract

Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media– and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.

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2020-04-01
2024-03-29
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Literature Cited

  1. 1. 
    Althouse BM, Ng YY, Cummings DAT 2011. Prediction of dengue incidence using search query surveillance. PLOS Negl. Trop. Dis. 5:e1258
    [Google Scholar]
  2. 2. 
    Aphinyanaphongs Y, Lulejian A, Brown DP, Bonneau R, Krebs P 2016. Text classification for automatic detection of e-cigarette use and use for smoking cessation from Twitter: a feasibility pilot. Pac. Symp. Biocomput. 21:480–91
    [Google Scholar]
  3. 3. 
    Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC-H et al. 2016. Results from the Centers for Disease Control and Prevention's predict the 2013–2014 Influenza Season Challenge. BMC Infect. Dis. 16:357
    [Google Scholar]
  4. 4. 
    Biggerstaff M, Johansson M, Alper D, Brooks LC, Chakraborty P et al. 2018. Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics 24:26–33
    [Google Scholar]
  5. 5. 
    Biggerstaff M, Kniss K, Jernigan DB, Brammer L, Bresee J et al. 2018. Systematic assessment of multiple routine and near real-time indicators to classify the severity of influenza seasons and pandemics in the United States, 2003–2004 through 2015–2016. Am. J. Epidemiol. 187:1040–50
    [Google Scholar]
  6. 6. 
    Böl G-F. 2016. Risk communication in times of crisis: pitfalls and challenges in ensuring preparedness instead of hysterics. EMBO Rep 17:1–9
    [Google Scholar]
  7. 7. 
    Bragazzi NL, Mahroum N. 2019. Google Trends predicts present and future plague cases during the plague outbreak in Madagascar: infodemiological study. JMIR Public Health Surveill 5:e13142
    [Google Scholar]
  8. 8. 
    Brammer L, Blanton L, Epperson S, Mustaquim D, Bishop A et al. 2011. Surveillance for influenza during the 2009 influenza A (H1N1) pandemicUnited States, April 2009–March 2010. Clin. Infect. Dis. 52:S27–35
    [Google Scholar]
  9. 9. 
    Broniatowski DA, Paul MJ, Dredze M 2013. National and local influenza surveillance through Twitter: an analysis of the 2012–2013 influenza epidemic. PLOS ONE 8:e83672
    [Google Scholar]
  10. 10. 
    Brownstein JS, Freifeld CC, Madoff LC 2009. Digital disease detection—harnessing the Web for public health surveillance. N. Engl. J. Med. 360:2153–552157
    [Google Scholar]
  11. 11. 
    Brownstein JS, Freifeld CC, Reis BY, Mandl KD 2008. Surveillance sans frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLOS Med 5:e151
    [Google Scholar]
  12. 12. 
    Buczak AL, Baugher B, Moniz LJ, Bagley T, Babin SM, Guven E 2018. Ensemble method for dengue prediction. PLOS ONE 13:e0189988
    [Google Scholar]
  13. 13. 
    Butler D. 2013. When Google got flu wrong. Nature 494:155–56
    [Google Scholar]
  14. 14. 
    Carlson SJ, Durrheim DN, Dalton CB 2010. Flutracking provides a measure of field influenza vaccine effectiveness, Australia, 2007–2009. Vaccine 28:6809–10
    [Google Scholar]
  15. 15. 
    Carneiro HA, Mylonakis E. 2009. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin. Infect. Dis. 49:1557–64
    [Google Scholar]
  16. 16. 
    CDC (Cent. Dis. Control Prev.) 2013. CDC competition encourages use of social media to predict flu Press Release Nov. 25. https://www.cdc.gov/flu/news/predict-flu-challenge.htm
  17. 17. 
    CDC (Cent. Dis. Control Prev.) 2019. Overview of influenza surveillance in the United States. Centers for Disease Control and Prevention, Influenza (Flu) https://www.cdc.gov/flu/weekly/overview.htm
    [Google Scholar]
  18. 18. 
    CDC (Cent. Dis. Control Prev.) Epidemic Predict. Initiat 2019. FluSight 2018–2019. Seasonal influence forecasting at the national and regional level. Epidemic Prediction Initiative https://predict.cdc.gov/post/5ba1504e5619f003acb7e18f
    [Google Scholar]
  19. 19. 
    Chan EH, Sahai V, Conrad C, Brownstein JS 2011. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLOS Negl. Trop. Dis. 5:e1206
    [Google Scholar]
  20. 20. 
    Choi BCK. 2012. The past, present, and future of public health surveillance. Scientifica 2012:875253
    [Google Scholar]
  21. 21. 
    Chretien J-P, George D, Shaman J, Chitale RA, McKenzie FE 2014. Influenza forecasting in human populations: a scoping review. PLOS ONE 9:e94130
    [Google Scholar]
  22. 22. 
    Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B 2019. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110:12–22
    [Google Scholar]
  23. 23. 
    Chunara R, Aman S, Smolinski M, Brownstein JS 2013. Flu Near You: an online self-reported influenza surveillance system in the USA. Online J. Public Health Inform. 5:e133
    [Google Scholar]
  24. 24. 
    Confessore N. 2018. Cambridge Analytica and Facebook: the scandal and the fallout so far. The New York Times April 4. https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html
    [Google Scholar]
  25. 25. 
    Cook S, Conrad C, Fowlkes AL, Mohebbi MH 2011. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLOS ONE 6:e23610
    [Google Scholar]
  26. 26. 
    Correia RB, Li L, Rocha LM 2016. Monitoring potential drug interactions and reactions via network analysis of Instagram user timelines. Pac. Symp. Biocomput. 21:492–503
    [Google Scholar]
  27. 27. 
    Del Valle SY, McMahon BH, Asher J, Hatchett R, Lega JC et al. 2018. Summary results of the 2014–2015 DARPA Chikungunya challenge. BMC Infect. Dis. 18:245
    [Google Scholar]
  28. 28. 
    Denecke K. 2017. An ethical assessment model for digital disease detection technologies. Life Sci. Soc. Policy 13:16
    [Google Scholar]
  29. 29. 
    Desai R, Hall AJ, Lopman BA, Shimshoni Y, Rennick M et al. 2012. Norovirus disease surveillance using Google Internet query share data. Clin. Infect. Dis. 55:e75–78
    [Google Scholar]
  30. 30. 
    Dietterich TG. 2000. Ensemble methods in machine learning. Multiple Classifier Systems J Kittler, F Roli 1–15 Berlin/Heidelberg: Springer
    [Google Scholar]
  31. 31. 
    DiMaggio P, Hargittai E, Neuman WR, Robinson JP 2001. Social implications of the Internet. Annu. Rev. Sociol. 27:307–36
    [Google Scholar]
  32. 32. 
    Eames KTD, Brooks-Pollock E, Paolotti D, Perosa M, Gioannini C, Edmunds WJ 2012. Rapid assessment of influenza vaccine effectiveness: analysis of an Internet-based cohort. Epidemiol. Infect. 140:1309–15
    [Google Scholar]
  33. 33. 
    Eckmanns T, Füller H, Roberts SL 2019. Digital epidemiology and global health security; an interdisciplinary conversation. Life Sci. Soc. Policy 15:2
    [Google Scholar]
  34. 34. 
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–18
    [Google Scholar]
  35. 36. 
    Eysenbach G. 2006. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu. Symp. Proc 2006.244–48
    [Google Scholar]
  36. 37. 
    Fairchild AL, Haghdoost AA, Bayer R, Selgelid MJ, Dawson A et al. 2017. Ethics of public health surveillance: new guidelines. Lancet Public Health 2:e348–49
    [Google Scholar]
  37. 38. 
    Flu Trends Team 2015. The next chapter for flu trends. Google AI Blog Aug. 20. https://ai.googleblog.com/2015/08/the-next-chapter-for-flu-trends.html
    [Google Scholar]
  38. 39. 
    Fried D, Surdeanu M, Kobourov S, Hingle M, Bell D 2014. Analyzing the language of food on social media. arXiv:1409.2195v2 [cs.CL]
  39. 40. 
    Geneviève LD, Martani A, Wangmo T, Paolotti D, Koppeschaar C et al. 2019. Participatory disease surveillance systems: ethical framework. J. Med. Internet Res. 21:e12273
    [Google Scholar]
  40. 41. 
    Gilman SL. 1987. AIDS and syphilis: the iconography of disease. October 43:87–107
    [Google Scholar]
  41. 42. 
    Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L 2009. Detecting influenza epidemics using search engine query data. Nature 457:1012–14
    [Google Scholar]
  42. 43. 
    Gittelman S, Lange V, Gotway Crawford CA, Okoro CA, Lieb E et al. 2015. A new source of data for public health surveillance: Facebook likes. J. Med. Internet Res. 17:e98
    [Google Scholar]
  43. 44. 
    Graham M, Hale S, Stephens M 2012. Featured graphic: Digital divide: the geography of Internet access. Environ. Plan. A 44:1009–10
    [Google Scholar]
  44. 45. 
    Guerrisi C, Turbelin C, Blanchon T, Hanslik T, Bonmarin I et al. 2016. Participatory syndromic surveillance of influenza in Europe. J. Infect. Dis. 214:S386–92
    [Google Scholar]
  45. 46. 
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–10
    [Google Scholar]
  46. 47. 
    Harris JK, Hawkins JB, Nguyen L, Nsoesie EO, Tuli G et al. 2017. Using Twitter to identify and respond to food poisoning: the Food Safety STL Project. J. Public Health Manag. Pract. 23:577–80
    [Google Scholar]
  47. 48. 
    Harris JK, Mansour R, Choucair B, Olson J, Nissen C et al. 2014. Health department use of social media to identify foodborne illness—Chicago, Illinois, 2013–2014. MMWR 63:681–85
    [Google Scholar]
  48. 49. 
    Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V et al. 2014. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—New York City, 2012–2013. MMWR 63:441–45
    [Google Scholar]
  49. 50. 
    Heitmueller A, Henderson S, Warburton W, Elmagarmid A, Pentland AS, Darzi A 2014. Developing public policy to advance the use of big data in health care. Health Aff 33:1523–30
    [Google Scholar]
  50. 51. 
    Höhle M. 2017. A statistician's perspective on digital epidemiology. Life Sci. Soc. Policy 13:17
    [Google Scholar]
  51. 52. 
    Holland S. 2015. Public Health Ethics Cambridge, UK: Polity
  52. 53. 
    Huang D-C, Wang J-F, Huang J-X, Sui DZ, Zhang H-Y et al. 2016. Towards identifying and reducing the bias of disease information extracted from search engine data. PLOS Comput. Biol. 12:e1004876
    [Google Scholar]
  53. 54. 
    Johnson HA, Wagner MM, Hogan WR, Chapman W, Olszewski RT et al. 2004. Analysis of Web access logs for surveillance of influenza. Stud. Health Technol. Inform. 107:1202–6
    [Google Scholar]
  54. 55. 
    Kandula S, Pei S, Shaman J 2019. Improved forecasts of influenza-associated hospitalization rates with Google Search Trends. J. R. Soc. Interface 16:20190080
    [Google Scholar]
  55. 56. 
    Kelly H, Grant K. 2009. Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination. Euro Surveill 14: https://doi.org/10.2807/ese.14.31.19288-en
    [Crossref] [Google Scholar]
  56. 57. 
    Klingler C, Silva DS, Schuermann C, Reis AA, Saxena A, Strech D 2017. Ethical issues in public health surveillance: a systematic qualitative review. BMC Public Health 17:295
    [Google Scholar]
  57. 58. 
    Koppeschaar CE, Colizza V, Guerrisi C, Turbelin C, Duggan J et al. 2017. Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe. JMIR Public Health Surveill 3:e66
    [Google Scholar]
  58. 59. 
    Kostkova P. 2018. Disease surveillance data sharing for public health: the next ethical frontiers. Life Sci. Soc. Policy 14:16
    [Google Scholar]
  59. 60. 
    Lazer D, Kennedy R, King G, Vespignani A 2014. The parable of Google Flu: traps in big data analysis. Science 343:1203–5
    [Google Scholar]
  60. 61. 
    Leetaru K, Wang S, Cao G, Padmanabhan A, Shook E 2013. Mapping the global Twitter heartbeat: the geography of Twitter. First Monday 18:5 https://doi.org/10.5210/fm.v18i5.4366
    [Crossref] [Google Scholar]
  61. 62. 
    Li Z, Liu T, Zhu G, Lin H, Zhang Y et al. 2017. Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: a case study in Guangzhou, China. PLOS Negl. Trop. Dis. 11:e0005354
    [Google Scholar]
  62. 63. 
    Madoff LC, Woodall JP. 2005. The Internet and the global monitoring of emerging diseases: lessons from the first 10 years of ProMED-mail. Arch. Med. Res. 36:724–30
    [Google Scholar]
  63. 64. 
    Marckmann G, Schmidt H, Sofaer N, Strech D 2015. Putting public health ethics into practice: a systematic framework. Front. Public Health 3:23
    [Google Scholar]
  64. 65. 
    McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M et al. 2019. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. Sci. Rep. 9:683
    [Google Scholar]
  65. 66. 
    Mignan A, Broccardo M. 2019. One neuron is more informative than a deep neural network for aftershock pattern forecasting. Nature 574:E1–3
    [Google Scholar]
  66. 67. 
    Mittelstadt B, Benzler J, Engelmann L, Prainsack B, Vayena E 2018. Is there a duty to participate in digital epidemiology?. Life Sci. Soc. Policy 14:9
    [Google Scholar]
  67. 68. 
    Mooney SJ, Pejaver V. 2018. Big data in public health: terminology, machine learning, and privacy. Annu. Rev. Public Health 39:95–112
    [Google Scholar]
  68. 69. 
    Nsoesie EO, Buckeridge DL, Brownstein JS 2014. Guess who's not coming to dinner? Evaluating online restaurant reservations for disease surveillance. J. Med. Internet Res. 16:e22
    [Google Scholar]
  69. 70. 
    Nsoesie EO, Gordon SA, Brownstein JS 2014. Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Prev. Med. 67:264–69
    [Google Scholar]
  70. 71. 
    Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L 2013. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLOS Comput. Biol. 9:e1003256
    [Google Scholar]
  71. 72. 
    Park H-A, Jung H, On J, Park SK, Kang H 2018. Digital epidemiology: use of digital data collected for non-epidemiological purposes in epidemiological studies. Healthc. Inform. Res. 24:253–62
    [Google Scholar]
  72. 73. 
    Pavalanathan U, Eisenstein J. 2015. Confounds and consequences in geotagged Twitter data. arXiv:1506.02275v2 [cs.CL]
  73. 74. 
    Perrin A, Anderson M. 2019. Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018 Pew Res. Cent., FactTank April 10. https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-social-media-including-facebook-is-mostly-unchanged-since-2018/
  74. 75. 
    Polgreen PM, Chen Y, Pennock DM, Nelson FD 2008. Using Internet searches for influenza surveillance. Clin. Infect. Dis. 47:1443–48
    [Google Scholar]
  75. 76. 
    Porta M 2008. A Dictionary of Epidemiology New York: Oxford Univ. Press
  76. 77. 
    Priedhorsky R, Osthus D, Daughton AR, Moran KR, Generous N et al. 2017. Measuring global disease with Wikipedia. CSCW Conf. Support. Coop. Work. 2017:1812–34
    [Google Scholar]
  77. 78. 
    Quinn P. 2018. Crisis communication in public health emergencies: the limits of ‘legal control’ and the risks for harmful outcomes in a digital age. Life Sci. Soc. Policy 14:4
    [Google Scholar]
  78. 79. 
    Reich NG, Brooks LC, Fox SJ, Kandula S, McGowan CJ et al. 2019. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. PNAS 116:3146–54
    [Google Scholar]
  79. 80. 
    Rocklöv J, Tozan Y, Ramadona A, Sewe MO, Sudre B et al. 2019. Using big data to monitor the introduction and spread of Chikungunya, Europe, 2017. Emerg. Infect. Dis. J. 25:1041
    [Google Scholar]
  80. 81. 
    Rodríguez-Martínez M, Garzón-Alfonso CC. 2018. Twitter Health Surveillance (THS) system. Proc. IEEE Int. Conf. Big Data 2018:1647–54
    [Google Scholar]
  81. 82. 
    Rogers EM. 2003. Diffusion of Innovations New York: Simon and Schuster, 5th ed..
  82. 83. 
    Sadilek A, Caty S, DiPrete L, Mansour R, Schenk T Jr et al. 2018. Machine-learned epidemiology: real-time detection of foodborne illness at scale. npj Digit. Med. 1:36
    [Google Scholar]
  83. 84. 
    Salathé M. 2018. Digital epidemiology: What is it, and where is it going. Life Sci. Soc. Policy 14:1
    [Google Scholar]
  84. 85. 
    Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS et al. 2012. Digital epidemiology. PLOS Comput. Biol. 8:e1002616
    [Google Scholar]
  85. 86. 
    Salathé M, Freifeld CC, Mekaru SR, Tomasulo AF, Brownstein JS 2013. Influenza A (H7N9) and the importance of digital epidemiology. N. Engl. J. Med. 369:401–4
    [Google Scholar]
  86. 87. 
    Salathé M, Khandelwal S. 2011. Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLOS Comput. Biol. 7:e1002199
    [Google Scholar]
  87. 88. 
    Santana MA, Dancy BL. 2000. The stigma of being named “AIDS carriers” on Haitian-American women. Health Care Women Int 21:161–71
    [Google Scholar]
  88. 89. 
    Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS 2015. Combining search, social media, and traditional data sources to improve influenza surveillance. PLOS Comput. Biol. 11:e1004513
    [Google Scholar]
  89. 90. 
    Santillana M, Zhang DW, Althouse BM, Ayers JW 2014. What can digital disease detection learn from (an external revision to) Google Flu Trends?. Am. J. Prev. Med. 47:341–47
    [Google Scholar]
  90. 91. 
    Scarpino SV, Dimitrov NB, Meyers LA 2012. Optimizing provider recruitment for influenza surveillance networks. PLOS Comput. Biol. 8:e1002472
    [Google Scholar]
  91. 92. 
    Shah MP, Lopman BA, Tate JE, Harris J, Esparza-Aguilar M et al. 2018. Use of Internet search data to monitor rotavirus vaccine impact in the United States, United Kingdom, and Mexico. J. Pediatr. Infect. Dis. Soc. 7:56–63
    [Google Scholar]
  92. 93. 
    Shaman J, Karspeck A. 2012. Forecasting seasonal outbreaks of influenza. PNAS 109:20425–30
    [Google Scholar]
  93. 94. 
    Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M 2013. Real-time influenza forecasts during the 2012–2013 season. Nat. Commun. 4:2837
    [Google Scholar]
  94. 95. 
    Simonsen L, Gog JR, Olson D, Viboud C 2016. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. J. Infect. Dis. 214:S380–85
    [Google Scholar]
  95. 96. 
    Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM et al. 2015. Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Health 105:2124–30
    [Google Scholar]
  96. 97. 
    Stoové MA, Pedrana AE. 2014. Making the most of a brave new world: opportunities and considerations for using Twitter as a public health monitoring tool. Prev. Med. 63:109–11
    [Google Scholar]
  97. 98. 
    Tufekci Z. 2014. Big questions for social media big data: representativeness, validity and other methodological pitfalls. arXiv:1403.7400 [cs.SI]
  98. 99. 
    Uiters E, Devillé W, Foets M, Spreeuwenberg P, Groenewegen PP 2009. Differences between immigrant and non-immigrant groups in the use of primary medical care; a systematic review. BMC Health Serv. Res. 9:76
    [Google Scholar]
  99. 100. 
    van der Laan MJ, Polley EC, Hubbard AE 2007. Super learner. Stat. Appl. Genet. Mol. Biol. 6:25
    [Google Scholar]
  100. 101. 
    Vayena E, Salathé M, Madoff LC, Brownstein JS 2015. Ethical challenges of big data in public health. PLOS Comput. Biol. 11:e1003904
    [Google Scholar]
  101. 102. 
    Velasco E. 2018. Disease detection, epidemiology and outbreak response: the digital future of public health practice. Life Sci. Soc. Policy 14:7
    [Google Scholar]
  102. 103. 
    Viboud C, Sun K, Gaffey R, Ajelli M, Fumanelli L et al. 2018. The RAPIDD Ebola forecasting challenge: synthesis and lessons learnt. Epidemics 22:13–21
    [Google Scholar]
  103. 104. 
    Viboud C, Vespignani A. 2019. The future of influenza forecasts. PNAS 116:2802–4
    [Google Scholar]
  104. 105. 
    Wang J, Yang X, Cai H, Tan W, Jin C, Li L 2016. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6:27327
    [Google Scholar]
  105. 106. 
    Wang W, Rothschild D, Goel S, Gelman A 2015. Forecasting elections with non-representative polls. Int. J. Forecast. 31:980–91
    [Google Scholar]
  106. 107. 
    Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM et al. 2012. Quantifying the impact of human mobility on malaria. Science 338:267–70
    [Google Scholar]
  107. 108. 
    WHO (World Health Organ.) 2017. Public health surveillance. World Health Organization, Health Topics https://www.who.int/topics/public_health_surveillance/en/
    [Google Scholar]
  108. 109. 
    Widmer G, Kubat M. 1996. Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23:69–101
    [Google Scholar]
  109. 110. 
    Wilson N, Mason K, Tobias M, Peacey M, Huang QS, Baker M 2009. Interpreting “Google Flu Trends” data for pandemic H1N1 influenza: the New Zealand experience. Eurosurveillance 14:19386
    [Google Scholar]
  110. 111. 
    Wójcik OP, Brownstein JS, Chunara R, Johansson MA 2014. Public health for the people: participatory infectious disease surveillance in the digital age. Emerg. Themes Epidemiol. 11:7
    [Google Scholar]
  111. 112. 
    Wood-Doughty Z, Andrews N, Marvin R, Dredze M 2018. Predicting Twitter user demographics from names alone. Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media105–11 Stroudsburg, PA: Assoc. Comp. Linguist.
    [Google Scholar]
  112. 113. 
    Yan SJ, Chughtai AA, Macintyre CR 2017. Utility and potential of rapid epidemic intelligence from Internet-based sources. Int. J. Infect. Dis. 63:77–87
    [Google Scholar]
  113. 114. 
    Yang S, Santillana M, Brownstein JS, Gray J, Richardson S, Kou SC 2017. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect. Dis. 17:332
    [Google Scholar]
  114. 115. 
    Yang S, Santillana M, Kou SC 2015. Accurate estimation of influenza epidemics using Google search data via ARGO. PNAS 112:14473–78
    [Google Scholar]
  115. 116. 
    Young SD, Torrone EA, Urata J, Aral SO 2018. Using search engine data as a tool to predict syphilis. Epidemiology 29:574–78
    [Google Scholar]
  116. 117. 
    Zhang L-C. 2000. Post-stratification and calibration—a synthesis. Am. Stat. 54:178–84
    [Google Scholar]
  117. 118. 
    Zhang Q, Perra N, Perrotta D, Tizzoni M, Paolotti D, Vespignani A 2017. Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model. Proceedings of the 26th International Conference on World Wide Web311–19 New York: ACM
    [Google Scholar]
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