Google trends in infodemiology and infoveillance: Methodology framework

JMIR Public Health and Surveillance

Volumn 5, Issue 2, 2019, Pages

  Mavragani, Amaryllis ^b   Ochoa, Gabriela ^a  

^a UNIVERSITY OF STIRLING   (United Kingdom)

^b NONE

Author keywords

Big data; Google Trends; Health; Infodemiology; Infoveillance; Internet behavior

Indexed keywords

EID: 85097170270     PISSN: None     EISSN: 23692960     Source Type: Journal    
DOI: 10.2196/13439     Document Type: Review

References (67)

1. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 2009;11(1):e11 [FREE Full text] [doi: 10.2196/jmir.1157] [Medline: 19329408]
2. Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med 2011 May;40(5 Suppl 2):S154-S158. [doi: 10.1016/j.amepre.2011.02.006] [Medline: 21521589]
3. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, et al. The use of google trends in health care research: a systematic review. PLoS One 2014;9(10):e109583 [FREE Full text] [doi: 10.1371/journal.pone.0109583] [Medline: 25337815]
4. Mavragani A, Ochoa G, Tsagarakis KP. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. J Med Internet Res 2018 Nov 06;20(11):e270 [FREE Full text] [doi: 10.2196/jmir.9366] [Medline: 30401664]
5. Gamma A, Schleifer R, Weinmann W, Buadze A, Liebrenz M. Could Google Trends Be Used to Predict Methamphetamine-Related Crime? An Analysis of Search Volume Data in Switzerland, Germany, and Austria. PLoS One 2016;11(11):e0166566 [FREE Full text] [doi: 10.1371/journal.pone.0166566] [Medline: 27902717]
6. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. J Big Data 2018 Sep 6;5(1). [doi: 10.1186/s40537-018-0140-9]
7. Mavragani A, Sampri A, Sypsa K, Tsagarakis KP. Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era. JMIR Public Health Surveill 2018 Mar 12;4(1):e24 [FREE Full text] [doi: 10.2196/publichealth.8726] [Medline: 29530839]
8. Simpson SS, Adams N, Brugman CM, Conners TJ. Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study. JMIR Public Health Surveill 2018 Jan 08;4(1):e2 [FREE Full text] [doi: 10.2196/publichealth.7726] [Medline: 29311050]
9. Wongkoblap A, Vadillo MA, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. J Med Internet Res 2017 Dec 29;19(6):e228 [FREE Full text] [doi: 10.2196/jmir.7215] [Medline: 28663166]
10. Park SH, Hong SH. Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data. J Med Internet Res 2018 Oct 24;20(10):e11085 [FREE Full text] [doi: 10.2196/11085] [Medline: 30355555]
11. Ricard BJ, Marsch LA, Crosier B, Hassanpour S. Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram. J Med Internet Res 2018 Dec 06;20(12):e11817 [FREE Full text] [doi: 10.2196/11817] [Medline: 30522991]
12. Huesch M, Chetlen A, Segel J, Schetter S. Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study. J Med Internet Res 2017 Jun 09;19(6):e201 [FREE Full text] [doi: 10.2196/jmir.7508] [Medline: 28600279]
13. Chen T, Dredze M. Vaccine Images on Twitter: Analysis of What Images are Shared. J Med Internet Res 2018 Apr 03;20(4):e130 [FREE Full text] [doi: 10.2196/jmir.8221] [Medline: 29615386]
14. Farhadloo M, Winneg K, Chan MS, Hall Jamieson K, Albarracin D. Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States. JMIR Public Health Surveill 2018 Feb 09;4(1):e16 [FREE Full text] [doi: 10.2196/publichealth.8186] [Medline: 29426815]
15. van Lent LG, Sungur H, Kunneman FA, van de Velde B, Das E. Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter. J Med Internet Res 2017 Dec 13;19(6):e193 [FREE Full text] [doi: 10.2196/jmir.7219] [Medline: 28611015]
16. Du J, Tang L, Xiang Y, Zhi D, Xu J, Song HY, et al. Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models. J Med Internet Res 2018 Jul 09;20(7):e236 [FREE Full text] [doi: 10.2196/jmir.9413] [Medline: 29986843]
17. Sewalk KC, Tuli G, Hswen Y, Brownstein JS, Hawkins JB. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. J Med Internet Res 2018 Oct 12;20(10):e10043 [FREE Full text] [doi: 10.2196/10043] [Medline: 30314959]
18. Chen S, Xu Q, Buchenberger J, Bagavathi A, Fair G, Shaikh S, et al. Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic. JMIR Public Health Surveill 2018 Nov 22;4(4):e10827 [FREE Full text] [doi: 10.2196/10827] [Medline: 30467106]
19. Alvarez-Mon MA, Asunsolo Del Barco A, Lahera G, Quintero J, Ferre F, Pereira-Sanchez V, et al. Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study. J Med Internet Res 2018 May 28;20(5):e205 [FREE Full text] [doi: 10.2196/jmir.9582] [Medline: 29807880]
20. Roccetti M, Marfia G, Salomoni P, Prandi C, Zagari RM, Gningaye KFL, et al. Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts. JMIR Public Health Surveill 2017 Aug 09;3(3):e51 [FREE Full text] [doi: 10.2196/publichealth.7004] [Medline: 28793981]
21. Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, et al. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health Surveill 2018 Jan 09;4(1):e4 [FREE Full text] [doi: 10.2196/publichealth.8950] [Medline: 29317382]
22. Google Trends. URL:https://trends.google.com/trends/?geo=US [accessed 2019-01-10] [WebCite Cache ID 75LXQbBW2]
23. Mavragani A, Tsagarakis KP. YES or NO: Predicting the 2015 GReferendum results using Google Trends. Technological Forecasting and Social Change 2016 Aug;109:1-5. [doi: 10.1016/j.techfore.2016.04.028]
24. Mavragani A, Ochoa G. Forecasting AIDS prevalence in the United States using online search traffic data. J Big Data 2018 May 19;5(1). [doi: 10.1186/s40537-018-0126-7]
25. Tana JC, Kettunen J, Eirola E, Paakkonen H. Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data. JMIR Ment Health 2018 May 23;5(2):e43 [FREE Full text] [doi: 10.2196/mental.9152] [Medline: 29792291]
26. Solano P, Ustulin M, Pizzorno E, Vichi M, Pompili M, Serafini G, et al. A Google-based approach for monitoring suicide risk. Psychiatry Res 2016 Dec 30;246:581-586. [doi: 10.1016/j.psychres.2016.10.030] [Medline: 27837725]
27. Arora VS, Stuckler D, McKee M. Tracking search engine queries for suicide in the United Kingdom, 2004-2013. Public Health 2016 Aug;137:147-153. [doi: 10.1016/j.puhe.2015.10.015] [Medline: 26976489]
28. Zheluk A, Quinn C, Meylakhs P. Internet search and krokodil in the Russian Federation: an infoveillance study. J Med Internet Res 2014 Sep 18;16(9):e212 [FREE Full text] [doi: 10.2196/jmir.3203] [Medline: 25236385]
29. Zhang Z, Zheng X, Zeng DD, Leischow SJ. Tracking Dabbing Using Search Query Surveillance: A Case Study in the United States. J Med Internet Res 2016 Sep 16;18(9):e252 [FREE Full text] [doi: 10.2196/jmir.5802] [Medline: 27637361]
30. Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018 Mar 14;20(3):e85 [FREE Full text] [doi: 10.2196/jmir.9222] [Medline: 29540337]
31. Allem J, Dharmapuri L, Leventhal AM, Unger JB, Boley Cruz T. Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis. J Med Internet Res 2018 Nov 19;20(11):e11669 [FREE Full text] [doi: 10.2196/11669] [Medline: 30455162]
32. Berlinberg EJ, Deiner MS, Porco TC, Acharya NR. Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data. JMIR Public Health Surveill 2018 May 02;4(2):e10180 [FREE Full text] [doi: 10.2196/10180] [Medline: 29720364]
33. Bragazzi NL, Mahroum N. Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study. JMIR Public Health Surveill 2019 Mar 08;5(1):e13142 [FREE Full text] [doi: 10.2196/13142] [Medline: 30763255]
34. Foroughi F, Lam AK, Lim MSC, Saremi N, Ahmadvand A. "Googling" for Cancer: An Infodemiological Assessment of Online Search Interests in Australia, Canada, New Zealand, the United Kingdom, and the United States. JMIR Cancer 2016 May 04;2(1):e5 [FREE Full text] [doi: 10.2196/cancer.5212] [Medline: 28410185]
35. Gianfredi V, Bragazzi NL, Mahamid M, Bisharat B, Mahroum N, Amital H, et al. Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis. Public Health 2018 Dec;165:9-15. [doi: 10.1016/j.puhe.2018.09.001] [Medline: 30342281]
36. Hswen Y, Gopaluni A, Brownstein JS, Hawkins JB. Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study. JMIR Mhealth Uhealth 2019 Feb 12;7(2):e12264 [FREE Full text] [doi: 10.2196/12264] [Medline: 30747718]
37. Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum. JMIR Med Inform 2018 Nov 29;6(4):e45 [FREE Full text] [doi: 10.2196/medinform.9162] [Medline: 30497991]
38. Kandula S, Hsu D, Shaman J. Subregional Nowcasts of Seasonal Influenza Using Search Trends. J Med Internet Res 2017 Dec 06;19(11):e370 [FREE Full text] [doi: 10.2196/jmir.7486] [Medline: 29109069]
39. Keller MS, Mosadeghi S, Cohen ER, Kwan J, Spiegel BMR. Reproductive Health and Medication Concerns for Patients With Inflammatory Bowel Disease: Thematic and Quantitative Analysis Using Social Listening. J Med Internet Res 2018 Jun 11;20(6):e206 [FREE Full text] [doi: 10.2196/jmir.9870] [Medline: 29891471]
40. Mejova Y, Weber I, Fernandez-Luque L. Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study. JMIR Public Health Surveill 2018 Mar 28;4(1):e30 [FREE Full text] [doi: 10.2196/publichealth.7217] [Medline: 29592849]
41. Odlum M, Yoon S, Broadwell P, Brewer R, Kuang D. How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets. JMIR Public Health Surveill 2018 Nov 22;4(4):e10262 [FREE Full text] [doi: 10.2196/10262] [Medline: 30467102]
42. Phillips CA, Barz LA, Li Y, Schapira MM, Bailey LC, Merchant RM. Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study. J Med Internet Res 2018 Jan 08;20(1):e6 [FREE Full text] [doi: 10.2196/jmir.8870] [Medline: 29311051]
43. Poirier C, Lavenu A, Bertaud V, Campillo-Gimenez B, Chazard E, Cuggia M, et al. Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study. JMIR Public Health Surveill 2018 Dec 21;4(4):e11361 [FREE Full text] [doi: 10.2196/11361] [Medline: 30578212]
44. Radin M, Sciascia S. Infodemiology of systemic lupus erythematous using Google Trends. Lupus 2017 Jul;26(8):886-889. [doi: 10.1177/0961203317691372] [Medline: 28162030]
45. Vasconcellos-Silva PR, Carvalho DBF, Trajano V, de La Rocque LR, Sawada ACMB, Juvanhol LL. Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Brazil: Why Not a Pink February? JMIR Public Health Surveill 2017 Apr 06;3(2):e17 [FREE Full text] [doi: 10.2196/publichealth.7015] [Medline: 28385679]
46. Wakamiya S, Kawai Y, Aramaki E. Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study. JMIR Public Health Surveill 2018 Sep 25;4(3):e65 [FREE Full text] [doi: 10.2196/publichealth.8627] [Medline: 30274968]
47. Wang H, Chen D. Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data. JMIR Public Health Surveill 2018 Apr 06;4(2):e37 [FREE Full text] [doi: 10.2196/publichealth.7314] [Medline: 29625958]
48. Watad A, Watad S, Mahroum N, Sharif K, Amital H, Bragazzi NL, et al. Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams-Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior. JMIR Public Health Surveill 2019 Feb 28;5(1):e9176 [FREE Full text] [doi: 10.2196/publichealth.9176] [Medline: 30601755]
49. Xu C, Wang Y, Yang H, Hou J, Sun L, Zhang X, et al. Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study. J Med Internet Res 2019 Jan 29;21(1):e10677 [FREE Full text] [doi: 10.2196/10677] [Medline: 30694203]
50. Google Trends. How Trends data is adjusted URL:https://support.google.com/trends/answer/ 4365533?hl=en-GB&ref_topic=6248052 [accessed 2019-01-10] [WebCite Cache ID 75LXW9Qoh]
51. Scharkow M, Vogelgesang J. Measuring the Public Agenda using Search Engine Queries. International Journal of Public Opinion Research 2011 Mar 01;23(1):104-113. [doi: 10.1093/ijpor/edq048]
52. Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, Buckee C, et al. Digital epidemiology. PLoS Comput Biol 2012 Jul;8(7):e1002616 [FREE Full text] [doi: 10.1371/journal.pcbi.1002616] [Medline: 22844241]
53. Brownstein J, Freifeld CC, Madoff LC. Digital disease detection-harnessing the Web for public health surveillance. N Engl J Med 2009 May 21;360(21):2153-5,2157 [FREE Full text] [doi: 10.1056/NEJMp0900702] [Medline: 19423867]
54. Chunara R, Goldstein E, Patterson-Lomba O, Brownstein JS. Estimating influenza attack rates in the United States using a participatory cohort. Sci Rep 2015 Apr 2;5(1). [doi: 10.1038/srep09540] [Medline: 25835538]
55. Leal-Neto OB, Dimech GS, Libel M, Oliveira W, Ferreira JP. Digital disease detection and participatory surveillance: overview and perspectives for Brazil. Rev Saude Publica 2016;50:17 [FREE Full text] [doi: 10.1590/S1518-8787.2016050006201] [Medline: 27191153]
56. Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping review on search queries and social media for disease surveillance: a chronology of innovation. J Med Internet Res 2013;15(7):e147 [FREE Full text] [doi: 10.2196/jmir.2740] [Medline: 23896182]
57. Google Flu Trends Data. URL:https://www.google.org/flutrends/about/ [accessed 2019-02-09] [WebCite Cache ID 763FITMeo]
58. Carneiro H, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009 Nov 15;49(10):1557-1564. [doi: 10.1086/630200] [Medline: 19845471]
59. Dugas AF, Jalalpour M, Gel Y, Levin S, Torcaso F, Igusa T, et al. Influenza forecasting with Google Flu Trends. PLoS One 2013 Feb;8(2):e56176 [FREE Full text] [doi: 10.1371/journal.pone.0056176] [Medline: 23457520]
60. Eurosurveillance editorial team. Google Flu Trends includes 14 European countries. Euro Surveill 2009;14(40) [FREE Full text] [doi: 10.2807/ese.14.40.19352-en]
61. Mavragani A, Ochoa G. The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak. BDCC 2018 Jan 16;2(1):2. [doi: 10.3390/bdcc2010002]
62. Preis T, Moat HS, Stanley HE, Bishop SR. Quantifying the advantage of looking forward. Sci Rep 2012;2:350 [FREE Full text] [doi: 10.1038/srep00350] [Medline: 22482034]
63. Preis T, Moat HS, Stanley HE. Quantifying trading behavior in financial markets using Google Trends. Sci Rep 2013;3:1684 [FREE Full text] [doi: 10.1038/srep01684] [Medline: 23619126]
64. Mavragani A, Sypsa K, Sampri A, Tsagarakis K. Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics. Water 2016 Nov 18;8(11):542. [doi: 10.3390/w8110542]
65. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc 2006:244-248 [FREE Full text] [Medline: 17238340]
66. Jun S, Yoo HS, Choi S. Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change 2018 May;130:69-87. [doi: 10.1016/j.techfore.2017.11.009]
67. Mavragani A, Tsagarakis KP. Predicting referendum results in the Big Data Era. J Big Data 2019 Jan 14;6:3. [doi: 10.1186/s40537-018-0166-z]