Assessing Ebola-related web search behaviour: Insights and implications from an analytical study of Google Trends-based query volumes

Infectious Diseases of Poverty

Volumn 4, Issue 1, 2015, Pages

  Alicino, Cristiano ^a   Bragazzi, Nicola Luigi ^a   Faccio, Valeria ^a   Amicizia, Daniela ^a   Panatto, Donatella ^a   Gasparini, Roberto ^a   Icardi, Giancarlo ^a,b   Orsi, Andrea ^a,b  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

Ebola; Google Trends; Human Development Index; Internet; Relative search volume; Web; West Africa

Indexed keywords

ARTICLE; CLINICAL ASSESSMENT; CONTROL STRATEGY; CROSS-SECTIONAL STUDY; DEMOGRAPHY; DISEASE CONTROL; DISEASE SURVEILLANCE; EBOLA HEMORRHAGIC FEVER; HEALTH CARE POLICY; HEALTH EDUCATION; HEALTH PROMOTION; HUMAN; INFORMATION PROCESSING; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PUBLIC HEALTH PROBLEM; STATISTICAL ANALYSIS; AFRICA; HEALTH SURVEY; HEMORRHAGIC FEVER, EBOLA; INTERNET; PROCEDURES; SEARCH ENGINE; WEB BROWSER;

AFRICA, WESTERN; HEMORRHAGIC FEVER, EBOLA; HUMANS; INTERNET; POPULATION SURVEILLANCE; SEARCH ENGINE; WEB BROWSER;

EID: 84962424457     PISSN: None     EISSN: 20499957     Source Type: Journal    
DOI: 10.1186/s40249-015-0090-9     Document Type: Article

References (40)

1. World Health Organization. Ebola Situation Report - 12 August 2015. Available at http://apps.who.int/ebola/current-situation/ebola-situation-report-12-august-2015. Accessed 17th August 2015.
2. Yahoo Tech. Top 10 Searches of 2014. Available at https://www.yahoo.com/tech/top-10-searches-of-2014-c1417565661893.html. Accessed 17th August 2015.
3. Google. Available at http://www.google.co.uk/trends/2014/story/ebola.html. Accessed 17th August 2015.
4. Fernández-Luque L, Bau T. Health and social media: perfect storm of information. Health Inform Res. 2015;21:67-73.
5. Fung IC, Fu KW, Ying Y, Schaible B, Hao Y, Chan CH, et al. Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks. Infect Dis Poverty. 2013;2:31.
6. Kristoufek L. Can Google Trends search queries contribute to risk diversification? Sci Rep. 2013;3:2713.
7. 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:e109583.
8. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012-4.
9. Google Flu Trends. Available at https://www.google.org/flutrends/intl/en_us/. Accessed 17th August 2015)
10. Jena AB, Karaca-Mandic P, Weaver L, Seabury SA. Predicting new diagnoses of HIV infection using internet search engine data. Clin Infect Dis. 2013;56:1352-3.
11. Samaras L, Garcia-Barriocanal E, Sicilia MA. Syndromic surveillance models using Web data: The case of scarlet fever in the UK. Inform Health Soc Care. 2012;37:106-24.
12. Ocampo AJ, Chunara R, Brownstein JS. Using search queries for malaria surveillance. Thailand Malar J. 2013;12:390.
13. Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis. 2011;5:e1206.
14. Althouse BM, Ng YY, Cummings DA. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis. 2011;5:e125.
15. Gluskin RT, Johansson MA, Santillana M, Brownstein JS. Evaluation of Internet-based dengue query data: Google Dengue Trends. PLoS Negl Trop Dis. 2014;8:e2713.
16. Cha Y, Stow CA. Mining web-based data to assess public response to environmental events. Environ Pollut. 2015;198:97-9.
17. Mytton OT, Rutter PD, Donaldson LJ. Influenza A(H1N1)pdm09 in England, 2009 to 2011: a greater burden of severe illness in the year after the pandemic than in the pandemic year. Eurosurveillance. 2012;17:20139.
18. Seifter A, Schwarzwalder A, Geis K, Aucott J. The utility of "Google Trends" for epidemiological research: Lyme disease as an example. Geospatial Health. 2010;4:135-7.
19. Polkowska A, Harjunpaa A, Toikkanen S, Lappalainen M, Vuento R, et al. Increased incidence of Mycoplasma pneumoniae infection in Finland, 2010-2011. Eurosurveillance. 2012;17:8-11.
20. Househ M. Communicating Ebola through social media and electronic news media outlets: A cross-sectional study. Health Informatics J. In press 2015.
21. Smallwood C. The Complete Guide to Using Google in Libraries: Instruction, Administration and Staff Productivity. 1st ed. Rowman & Littlefield. Lanham,Maryland,USA. 2015.
22. Taylor R. Interpretation of the Correlation Coefficient: A Basic Review. J Def Model Simul. 1990;1:35-9.
23. Warf B. Uneven geographies of the African Internet: growth, change, and implications. Afr Geogr Rev. 2010;29:41-66.
24. Aker JC, Mbiti IM. Mobile Phones and Economic Development in Africa. 2010. CGD Working Paper 211. Washington DC: Center for Global Development. Available at http://www.cgdev.org/content/publications/detail/1424175. Accessed 17th August 2015.
25. Griffin H. Has Mobile Phone Technology Had an Impact on the Quality of Life in the Developing World? Int J Acad Res Bus Soc Sci. 2013;3:7-14.
26. Ssewanyana JK. Internet Usage and Human Development in Africa: A Cross-Regional Analysis. Int. J Comp Sci Emerging Tech. 2011;2:470-5.
27. Rodriguez-Morales AJ, Castañeda-Hernández DM, McGregor A. What makes people talk about Ebola on social media? A retrospective analysis of Twitter use. Travel Med Infect Dis. 2015;13:100-1.
28. Carter M. How Twitter may have helped Nigeria contain Ebola. BMJ. 2014;349:g6946.
29. The Lancet. The medium and the message of Ebola. Lancet. 2014;384:1641.
30. Fung IC, Tse ZT, Cheung CN, Miu AS, Fu KW. Ebola and the social media. Lancet. 2014;384:2207.
31. Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, et al. Mass Media and the Contagion of Fear: The Case of Ebola in America. PLoS One. 2015;10:e0129179.
32. Gao J, Yin L. Drug development for controlling Ebola epidemic - a race against time. Drug Discov Ther. 2014;8:229-31.
33. Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis. 2009;49:1557-64.
34. Wang JY, Bennett K, Probst J. Subdividing the digital divide: differences in internet access and use among rural residents with medical limitations. J Med Internet Res. 2011;13:e25.
35. Brodie M, Flournoy RE, Altman DE, Blendon RJ, Benson JM, Rosenbaum MD. Health information, the Internet, and the digital divide. Health Aff. 2000;19:255-65.
36. Wamala DS, Augustine K. A meta-analysis of telemedicine success in Africa. J Pathol Inform. 2013;4:6.
37. Salathé M, Freifeld CC, Mekaru SR, Tomasulo AF, Brownstein JS. Influenza A (H7N9) and the importance of digital epidemiology. N Engl J Med. 2013;369:401-4.
38. Eysenbach G. Infodemiology: The epidemiology of (mis)information. Am J Med. 2002;113:763-5.
39. Lazard AJ, Scheinfeld E, Bernhardt JM, Wilcox GB, Suran M. Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat. Am J Infect Control. 2015;S0196-6553(15):00614-8.
40. Linkov F, Freese KE, Bovbjerg DH, Edwards RP, Ramanathan R, Gourash W. Stakeholder involvement is essential for patient centered applications of Google Trends research. Surg Obes Relat Dis. 2014;10:370-1.