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

BackgroundThe 2014 Ebola epidemic in West Africa has attracted public interest worldwide, leading to millions of Ebola-related Internet searches being performed during the period of the epidemic. This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred. The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.MethodsGoogle Trends (GT) was used to explore Internet activity related to Ebola. The study period was from 29 December 2013 to 14 June 2015. Pearson’s correlation was performed to correlate Ebola-related relative search volumes (RSVs) with the number of weekly and overall Ebola cases. Multivariate regression was performed using Ebola-related RSV as a dependent variable, and the overall number of Ebola cases and the Human Development Index were used as predictor variables.ResultsThe greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic. The queries varied in the different countries. Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted, in relation to the different flux volumes and different time courses. In the affected African countries, web query search volumes were mostly concentrated in the capital areas. However, in Western countries, web queries were uniformly distributed over the national territory. In terms of the three countries mainly affected by the Ebola epidemic, the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate. The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.ConclusionGoogle Trends showed a coarse-grained nature, strongly correlating with global epidemiological data, but was weaker at country level, as it was prone to distortions induced by unbalanced media coverage and the digital divide. Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies, particularly in the case of health-threatening events.

[1]  Eleftherios Mylonakis,et al.  Google trends: a web-based tool for real-time surveillance of disease outbreaks. , 2009, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[2]  Pinar Karaca-Mandic,et al.  Predicting new diagnoses of HIV infection using internet search engine data. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[3]  H. Griffin Business Ethics Perceptions of Public and Private Sector Respondents in Pakistan , 2013 .

[4]  Emily H. Chan,et al.  Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance , 2011, PLoS neglected tropical diseases.

[5]  Mowafa Said Househ,et al.  Communicating Ebola through social media and electronic news media outlets: A cross-sectional study , 2016, Health Informatics J..

[6]  Craig A Stow,et al.  Mining web-based data to assess public response to environmental events. , 2015, Environmental pollution.

[7]  J. Aker,et al.  Mobile Phones and Economic Development in Africa , 2010 .

[8]  Ladislav Kristoufek,et al.  Can Google Trends search queries contribute to risk diversification? , 2013, Scientific Reports.

[9]  K. Schønning,et al.  Increased incidence of Mycoplasma pneumoniae infections detected by laboratory-based surveillance in Denmark in 2010. , 2010, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  Miguel-Angel Sicilia,et al.  Syndromic surveillance models using Web data: The case of scarlet fever in the UK , 2012, Informatics for health & social care.

[11]  A. Rodríguez-Morales,et al.  What makes people talk about Ebola on social media? A retrospective analysis of Twitter use. , 2015, Travel medicine and infectious disease.

[12]  T. Lancet The medium and the message of Ebola , 2014, The Lancet.

[13]  Jay M Bernhardt,et al.  Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat. , 2015, American journal of infection control.

[14]  H. Griffin Has Mobile Phone Technology Had an Impact on the Quality of Life in the Developing World? , 2013 .

[15]  D. Wamala,et al.  A meta-analysis of telemedicine success in Africa , 2013, Journal of pathology informatics.

[16]  Yi Hao,et al.  Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks , 2013, Infectious Diseases of Poverty.

[17]  R. Blendon,et al.  Health information, the Internet, and the digital divide. , 2000, Health affairs.

[18]  Luis Fernández-Luque,et al.  Health and Social Media: Perfect Storm of Information , 2015, Healthcare informatics research.

[19]  S. Nuti,et al.  The Use of Google Trends in Health Care Research: A Systematic Review , 2014, PloS one.

[20]  J. Brownstein,et al.  Using search queries for malaria surveillance, Thailand , 2013, Malaria Journal.

[21]  R. Vuento,et al.  Increased incidence of Mycoplasma pneumoniae infection in Finland, 2010-2011. , 2012, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[22]  Carol Smallwood The Complete Guide to Using Google in Libraries , 2015 .

[23]  John S. Brownstein,et al.  Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends , 2014, PLoS neglected tropical diseases.

[24]  D. Cummings,et al.  Prediction of Dengue Incidence Using Search Query Surveillance , 2011, PLoS neglected tropical diseases.

[25]  Richard Taylor Interpretation of the Correlation Coefficient: A Basic Review , 1990 .

[26]  Kevin Bennett,et al.  Subdividing the Digital Divide: Differences in Internet Access and Use among Rural Residents with Medical Limitations , 2011, Journal of medical Internet research.

[27]  Meg Carter,et al.  How Twitter may have helped Nigeria contain Ebola , 2014, BMJ : British Medical Journal.

[28]  Jianjun Gao,et al.  Drug development for controlling Ebola epidemic - a race against time. , 2014, Drug discoveries & therapeutics.

[29]  J. Aucott,et al.  The utility of "Google Trends" for epidemiological research: Lyme disease as an example. , 2010, Geospatial health.

[30]  G. Eysenbach Infodemiology: The epidemiology of (mis)information. , 2002, The American journal of medicine.

[31]  Carlos Castillo-Chavez,et al.  Mass Media and the Contagion of Fear: The Case of Ebola in America , 2015, PloS one.

[32]  L. Donaldson,et al.  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. , 2012, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[33]  Stakeholder involvement is essential for patient centered applications of Google Trends research. , 2014, Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery.

[34]  J. Brownstein,et al.  Influenza A (H7N9) and the importance of digital epidemiology. , 2013, The New England journal of medicine.

[35]  Zion Tsz Ho Tse,et al.  Ebola and the social media , 2014, The Lancet.

[36]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.