Enhancing seasonal influenza surveillance: Topic analysis of widely used medicinal drugs using twitter data

Journal of Medical Internet Research

Volumn 19, Issue 9, 2017, Pages

  Kagashe, Ireneus ^a   Yan, Zhijun ^a   Suheryani, Imran ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Disease outbreaks; Influenza; Influenza vaccines; Machine learning; Natural language processing; Public health surveillance; Social media; Twitter messaging

Indexed keywords

DRUG THERAPY; EPIDEMIC; HUMAN; INFLUENZA; MACHINE LEARNING; PROCEDURES; PUBLIC HEALTH; SOCIAL MEDIA; STATISTICS AND NUMERICAL DATA; UTILIZATION;

DISEASE OUTBREAKS; DRUG THERAPY; HUMANS; INFLUENZA, HUMAN; MACHINE LEARNING; PUBLIC HEALTH; SOCIAL MEDIA;

EID: 85031124625     PISSN: None     EISSN: 14388871     Source Type: Journal    
DOI: 10.2196/jmir.7393     Document Type: Article

References (70)

1. Porta M, Greenland S, Silva I, Last JM. A dictionary of epidemiology. New York: Oxford University Press; 2014.
2. Lee LM, Teutsch SM, Thacker SB, St Louis ME. Principles & practice of public health surveillance, 3rd edition. Oxford: Oxford University Press; 2010.
3. CDC. 2017. Seasonal influenza (flu): about flu URL: https://www.cdc.gov/flu/about/index.html [accessed 2017-08-19] [WebCite Cache ID _6sp9FqLKg]
4. CDC. 2016. CDC says “Take 3” actions to fight the flu URL: http://www.cdc.gov/flu/protect/preventing.htm [accessed 2016-11-16] [WebCite Cache ID _6m2KJkwd9]
5. Sugawara T, Ohkusa Y, Ibuka Y, Kawanohara H, Taniguchi K, Okabe N. Real-time prescription surveillance and its application to monitoring seasonal influenza activity in Japan. J Med Internet Res 2012 Jan 16;14(1):e14 [FREE Full _text] [doi: 10.2196/jmir.1881] [Medline: 22249906]
6. Seo D, Jo M, Sohn CH, Shin S, Lee J, Yu M, et al. Cumulative query method for influenza surveillance using search engine data. J Med Internet Res 2014 Dec 16;16(12):e289 [FREE Full _text] [doi: 10.2196/jmir.3680] [Medline: 25517353]
7. Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating google, twitter, and wikipedia as tools for influenza surveillance using bayesian change point analysis: a comparative analysis. JMIR Public Health Surveill 2016 Oct 20;2(2):e161 [FREE Full _text] [doi: 10.2196/publichealth.5901] [Medline: 27765731]
8. Twitter. 2016. Twitter usage/company facts URL: https://about.twitter.com/company [accessed 2016-11-15] [WebCite Cache ID _6m2MLDMe2]
9. Java A, Song X, Finin T, Tseng B. Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web mining and Social Network Analysis.: ACM; 2007 Presented at: WebKDD/SNA-KDD '07; August 12, 2017; San Jose, CA p. 56-65. [doi: 10.1145/1348549.1348556]
10. Yin Z, Fabbri D, Rosenbloom ST, Malin B. A scalable framework to detect personal health mentions on twitter. J Med Internet Res 2015;17(6):e138 [FREE Full _text] [doi: 10.2196/jmir.4305] [Medline: 26048075]
11. Aramaki E, Maskawa S, Morita M. Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011 Presented at: EMNLP 2011; July 27-31, 2011; Edinburgh p. 1568-1576.
12. Paul M, Dredze M. You are what you tweet: analyzing twitter for public health. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.: AAAI Press; 2011 Presented at: ICWSM-11; July 17-21, 2011; Barcelona p. 265-272 URL: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2880/3264
13. Hanson CL, Burton SH, Giraud-Carrier C, West JH, Barnes MD, Hansen B. Tweaking and tweeting: exploring Twitter for nonmedical use of a psychostimulant drug (Adderall) among college students. J Med Internet Res 2013;15(4):e62 [FREE Full _text] [doi: 10.2196/jmir.2503] [Medline: 23594933]
14. Daniulaityte R, Nahhas RW, Wijeratne S, Carlson RG, Lamy FR, Martins SS, et al. “Time for dabs”: analyzing twitter data on marijuana concentrates across the U.S. Drug Alcohol Depend 2015 Oct 1;155:307-311. [doi: 10.1016/j.drugalcdep.2015.07.1199] [Medline: 26338481]
15. Kendra RL, Karki S, Eickholt JL, Gandy L. Characterizing the discussion of antibiotics in the twittersphere: what is the bigger picture? J Med Internet Res 2015;17(6):e154 [FREE Full _text] [doi: 10.2196/jmir.4220] [Medline: 26091775]
16. Cole-Lewis H, Varghese A, Sanders A, Schwarz M, Pugatch J, Augustson E. Assessing electronic cigarette-related tweets for sentiment and content using supervised machine learning. J Med Internet Res 2015;17(8):e208 [FREE Full _text] [doi: 10.2196/jmir.4392] [Medline: 26307512]
17. Yepes A, MacKinlay A, Han B. Investigating public health surveillance using twitter. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015). 2015 Presented at: ACL-IJCNLP 2015; July 30, 2015; Beijing p. 164-170. [doi: 10.18653/v1/W15-3821]
18. Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P, et al. Influenza-like illness surveillance on twitter through automated learning of naïve language. PLoS One 2013;8(12):e82489 [FREE Full _text] [doi: 10.1371/journal.pone.0082489] [Medline: 24324799]
19. Broniatowski DA, Paul MJ, Dredze M. National and local influenza surveillance through twitter: an analysis of the 2012-2013 influenza epidemic. PLoS One 2013;8(12):e83672 [FREE Full _text] [doi: 10.1371/journal.pone.0083672] [Medline: 24349542]
20. Pivette M, Mueller JE, Crépey P, Bar-Hen A. Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review. BMC Infect Dis 2014 Nov 18;14:604 [FREE Full _text] [doi: 10.1186/s12879-014-0604-2] [Medline: 25403237]
21. Woo H, Cho Y, Shim E, Lee J, Lee C, Kim SH. Estimating influenza outbreaks using both search engine query data and social media data in South Korea. J Med Internet Res 2016;18(7):e177 [FREE Full _text] [doi: 10.2196/jmir.4955] [Medline: 27377323]
22. Hill S, Mao J, Ungar L, Hennessy S, Leonard CE, Holmes J. Natural supplements for H1N1 influenza: retrospective observational infodemiology study of information and search activity on the Internet. J Med Internet Res 2011 May 10;13(2):e36 [FREE Full _text] [doi: 10.2196/jmir.1722] [Medline: 21558062]
23. Signorini A, Segre AM, Polgreen PM. 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 2011;6(5):e19467 [FREE Full _text] [doi: 10.1371/journal.pone.0019467] [Medline: 21573238]
24. Salathé M, Khandelwal S. Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS Comput Biol 2011 Oct;7(10):e1002199 [FREE Full _text] [doi: 10.1371/journal.pcbi.1002199] [Medline: 22022249]
25. Aslam AA, Tsou M, Spitzberg BH, An L, Gawron JM, Gupta DK, et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance. J Med Internet Res 2014;16(11):e250 [FREE Full _text] [doi: 10.2196/jmir.3532] [Medline: 25406040]
26. Ji X, Chun SA, Wei Z, Geller J. Twitter sentiment classification for measuring public health concerns. Soc Netw Anal Min 2015 May 12;5(1):13. [doi: 10.1007/s13278-015-0253-5]
27. Weeg C, Schwartz HA, Hill S, Merchant RM, Arango C, Ungar L. Using twitter to measure public discussion of diseases: a case study. JMIR Public Health Surveill 2015;1(1):e6 [FREE Full _text] [doi: 10.2196/publichealth.3953] [Medline: 26925459]
28. Alvaro N, Conway M, Doan S, Lofi C, Overington J, Collier N. Crowdsourcing twitter annotations to identify first-hand experiences of prescription drug use. J Biomed Inform 2015 Dec;58:280-287 [FREE Full _text] [doi: 10.1016/j.jbi.2015.11.004] [Medline: 26556646]
29. Robertson C, Yee L. Avian influenza risk surveillance in North America with online media. PLoS One 2016;11(11):e0165688 [FREE Full _text] [doi: 10.1371/journal.pone.0165688] [Medline: 27880777]
30. Taddio A, Ipp M, Thivakaran S, Jamal A, Parikh C, Smart S, et al. Survey of the prevalence of immunization non-compliance due to needle fears in children and adults. Vaccine 2012 Jul 06;30(32):4807-4812. [doi: 10.1016/j.vaccine.2012.05.011] [Medline: 22617633]
31. Wheelock A, Parand A, Rigole B, Thomson A, Miraldo M, Vincent C, et al. Socio-psychological factors driving adult vaccination: a qualitative study. PLoS One 2014;9(12):e113503 [FREE Full _text] [doi: 10.1371/journal.pone.0113503] [Medline: 25490542]
32. Rossmann BE, Rench MA, Montesinos DP, Healy CM. Acceptability of immunization in adult contacts of infants: possibility of expanding platforms to increase adult vaccine uptake. Vaccine 2014 May 07;32(22):2540-2545. [doi: 10.1016/j.vaccine.2014.03.056] [Medline: 24681227]
33. Kim N, Mountain TP. Role of non-traditional locations for seasonal flu vaccination: empirical evidence and evaluation. Vaccine 2017 May 19;35(22):2943-2948. [doi: 10.1016/j.vaccine.2017.04.023] [Medline: 28433328]
34. Prematunge C, Corace K, McCarthy A, Nair RC, Roth V, Suh KN, et al. Qualitative motivators and barriers to pandemic vs. seasonal influenza vaccination among healthcare workers: a content analysis. Vaccine 2014 Dec 12;32(52):7128-7134. [doi: 10.1016/j.vaccine.2014.10.023] [Medline: 25454876]
35. Kelso JM. Influenza vaccine and egg allergy: nearing the end of an evidence-based journey. J Allergy Clin Immunol Pract 2015;3(1):140-141. [doi: 10.1016/j.jaip.2014.08.011] [Medline: 25577641]
36. Zerbo O, Qian Y, Yoshida C, Fireman BH, Klein NP, Croen LA. Association between influenza infection and vaccination during pregnancy and risk of autism spectrum disorder. JAMA Pediatr 2017 Jan 02;171(1):e163609. [doi: 10.1001/jamapediatrics.2016.3609] [Medline: 27893896]
37. Maher L, Dawson A, Wiley K, Hope K, Torvaldsen S, Lawrence G, et al. Influenza vaccination during pregnancy: a qualitative study of the knowledge, attitudes, beliefs, and practices of general practitioners in Central and South-Western Sydney. BMC Fam Pract 2014 May 23;15:102 [FREE Full _text] [doi: 10.1186/1471-2296-15-102] [Medline: 24884996]
38. Chamberlain A, Seib K, Ault K, Orenstein W, Frew P, Malik F, et al. Factors associated with intention to receive influenza and tetanus, diphtheria, and acellular pertussis (Tdap) vaccines during pregnancy: a focus on vaccine hesitancy and perceptions of disease severity and vaccine safety. PLoS Curr 2015 Feb 25;7:- [FREE Full _text] [doi: 10.1371/currents.outbreaks.d37b61bceebae5a7a06d40a301cfa819] [Medline: 25789203]
39. Bettinger JA, Greyson D, Money D. Attitudes and beliefs of pregnant women and new mothers regarding influenza vaccination in British Columbia. J Obstet Gynaecol Can 2016 Nov;38(11):1045-1052. [doi: 10.1016/j.jogc.2016.08.004] [Medline: 27969559]
40. Lau JT, Au DW, Tsui HY, Choi KC. Prevalence and determinants of influenza vaccination in the Hong Kong Chinese adult population. Am J Infect Control 2012 Sep;40(7):e225-e227. [doi: 10.1016/j.ajic.2012.01.036] [Medline: 22743046]
41. Lv M, Fang R, Wu J, Pang X, Deng Y, Lei T, et al. The free vaccination policy of influenza in Beijing, China: The vaccine coverage and its associated factors. Vaccine 2016 Apr 19;34(18):2135-2140. [doi: 10.1016/j.vaccine.2016.02.032] [Medline: 26917011]
42. Gargano L, Underwood N, Sales J, Seib K, Morfaw C, Murray D, et al. Influence of sources of information about influenza vaccine on parental attitudes and adolescent vaccine receipt. Hum Vaccin Immunother 2015;11(7):1641-1647 [FREE Full text] [doi: 10.1080/21645515.2015.1038445] [Medline: 25996686]
43. Admon L, Haefner JK, Kolenic GE, Chang T, Davis MM, Moniz MH. Recruiting pregnant patients for survey research: a head to head comparison of social media-based versus clinic-based approaches. J Med Internet Res 2016 Dec 21;18(12):e326 [FREE Full _text] [doi: 10.2196/jmir.6593] [Medline: 28003174]
44. Yang M, Tan EC. Web-based and telephone surveys to assess public perception toward the national health insurance in taiwan: a comparison of cost and results. Interact J Med Res 2015 Apr 17;4(2):e9 [FREE Full _text] [doi: 10.2196/ijmr.4090] [Medline: 25886989]
45. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 2015 Oct;11(10):e1004513 [FREE Full _text] [doi: 10.1371/journal.pcbi.1004513] [Medline: 26513245]
46. Guthmann J, Fonteneau L, Bonmarin I, Lévy-Bruhl D. Influenza vaccination coverage one year after the A(H1N1) influenza pandemic, France, 2010-2011. Vaccine 2012 Feb 01;30(6):995-997. [doi: 10.1016/j.vaccine.2011.12.011] [Medline: 22178520]
47. Allen C, Tsou M, Aslam A, Nagel A, Gawron J. Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS One 2016;11(7):e0157734 [FREE Full _text] [doi: 10.1371/journal.pone.0157734] [Medline: 27455108]
48. Tuarob S, Tucker C, Salathe M, Ram N. Discovering health-related knowledge in social media using ensembles of heterogeneous features. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013). New York: ACM; 2013 Presented at: CIKM 2013; October 27 - November 01, 2013; San Francisco p. 1685-1690. [doi: 10.1145/2505515.2505629]
49. Jain VK, Kumar S. An effective approach to track levels of influenza-A (H1N1) pandemic in India using twitter. Procedia Computer Science 2015;70:801-807. [doi: 10.1016/j.procs.2015.10.120]
50. Prieto VM, Matos S, Álvarez M, Cacheda F, Oliveira JL. Twitter: a good place to detect health conditions. PLoS One 2014;9(1):e86191 [FREE Full _text] [doi: 10.1371/journal.pone.0086191] [Medline: 24489699]
51. Aronson A. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 2001:17-21 [FREE Full _text] [Medline: 11825149]
52. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas 2016 Jul 02;20(1):37-46. [doi: 10.1177/001316446002000104]
53. De Marnefe MC, Manning CD. Stanford University. Stanford typed dependencies manual URL: https://nlp.stanford.edu/software/dependencies_manual.pdf [accessed 2017-08-18] [WebCite Cache ID _6so2F3bX4]
54. Manning C, Surdeanu M, Bauer J, Finkel J, Bethard S, McClosky D. The Stanford coreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics.: ACL; 2014 Presented at: System Demonstrations; June 23-24, 2014; Baltimore p. 55-60 URL: http://www.aclweb.org/anthology/P/P14/P14-5010[doi: 10.3115/v1/P14-5010]
55. Chang C, Lin C. LIBSVM. ACM Trans Intell Syst Technol 2011 Apr 01;2(3):1-27. [doi: 10.1145/1961189.1961199]
56. Blei DM. Probabilistic topic models. Commun ACM 2012 Apr 01;55(4):77. [doi: 10.1145/2133806.2133826]
57. Blei D, Ng A, Jordan M. Latent dirichlet allocation. J Mach Learn Res 2003;3(Jan):993-1022.
58. Mallet. 2002. MALLET: a machine learning for language toolkit URL: http://mallet.cs.umass.edu/ [accessed 2016-11-16] [WebCite Cache ID _6m3SO8CIp]
59. Graham S, Weingart S, Milligan I. The Programming Historian. 2012. Getting started with topic modeling and MALLET URL: https://programminghistorian.org/lessons/topic-modeling-and-mallet [accessed 2017-08-18] [WebCite Cache ID 6so8n0jwd]
60. Han YK, Michie S, Potts HW, Rubin GJ. Predictors of influenza vaccine uptake during the 2009/10 influenza A H1N1v ('swine flu') pandemic: results from five national surveys in the United Kingdom. Prev Med 2016 Mar;84:57-61 [FREE Full _text] [doi: 10.1016/j.ypmed.2015.12.018] [Medline: 26757401]
61. Bíró A. Determinants of H1N1 vaccination uptake in England. Prev Med 2013 Aug;57(2):140-142. [doi: 10.1016/j.ypmed.2013.04.017] [Medline: 23648526]
62. Johnson DR, Nichol KL, Lipczynski K. Barriers to adult immunization. Am J Med 2008 Jul;121(7 Suppl 2):S28-S35. [doi: 10.1016/j.amjmed.2008.05.005] [Medline: 18589065]
63. Giese C, Mereckiene J, Danis K, O'Donnell J, O'Flanagan D, Cotter S. Low vaccination coverage for seasonal influenza and pneumococcal disease among adults at-risk and health care workers in Ireland, 2013: The key role of GPs in recommending vaccination. Vaccine 2016 Jul 12;34(32):3657-3662. [doi: 10.1016/j.vaccine.2016.05.028] [Medline: 27255466]
64. Campbell H, Edwards A, Letley L, Bedford H, Ramsay M, Yarwood J. Changing attitudes to childhood immunisation in English parents. Vaccine 2017 May 19;35(22):2979-2985. [doi: 10.1016/j.vaccine.2017.03.089] [Medline: 28442229]
65. He L, Liao Q, Huang Y, Feng S, Zhuang X. Parents' perception and their decision on their children's vaccination against seasonal influenza in Guangzhou. Chin Med J (Engl) 2015 Feb 05;128(3):327-341. [doi: 10.4103/0366-6999.150099] [Medline: 25635428]
66. Chan T, Fu Y, Wang D, Chuang J. Determinants of receiving the pandemic (H1N1) 2009 vaccine and intention to receive the seasonal influenza vaccine in Taiwan. PLoS One 2014;9(6):e101083 [FREE Full _text] [doi: 10.1371/journal.pone.0101083] [Medline: 24971941]
67. Taddio A, McMurtry CM, Shah V, Riddell RP, Chambers CT, Noel M, HELPinKids&Adults. Reducing pain during vaccine injections: clinical practice guideline. CMAJ 2015 Sep 22;187(13):975-982 [FREE Full _text] [doi: 10.1503/cmaj.150391] [Medline: 26303247]
68. Taddio A, McMurtry CM, Shah V, Yoon EW, Uleryk E, Pillai RR, HELPinKids&Adults Team. Methodology for knowledge synthesis of the management of vaccination pain and needle fear. Clin J Pain 2015 Oct;31(10 Suppl):S12-S19 [FREE Full text] [doi: 10.1097/AJP.0000000000000263] [Medline: 26352917]
69. Eppes C, Wu A, You W, Cameron KA, Garcia P, Grobman W. Barriers to influenza vaccination among pregnant women. Vaccine 2013 Jun 12;31(27):2874-2878. [doi: 10.1016/j.vaccine.2013.04.031] [Medline: 23623863]
70. Evanovich LL, Scott TM. Facilitating PBIS implementation: an administrator's guide to presenting the logic and steps to faculty and staff. Beyond Behavior 2016 Apr;25(1):4-8. [doi: 10.1177/107429561602500102]