Internet Search Limitations and Pandemic Influenza, Singapore | CDC EID

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Volume 16, Number 10–October 2010
Letter
Internet Search Limitations and Pandemic Influenza, Singapore
Alex R. Cook , Mark I.C. Chen, and Raymond Tzer Pin Lin
Author affiliations: National University of Singapore, Singapore (A.R. Cook, M.I.C. Chen, R.T.P. Lin); Tan Tock Seng Hospital, Singapore (M.I.C. Chen); Duke-NUS Graduate Medical School, Singapore (M.I.C. Chen); and Ministry of Health, Singapore (R.T.P. Lin)

Suggested citation for this article

To the Editor: In the past few years, several publications have reported that Internet search queries may usefully supplement other, traditional surveillance programs for infectious diseases (1–3). The philanthropic arm of Google offers Flu Trends, a site that provides up-to-date estimates of influenza activity in 20 countries of the Pacific Rim and Europe (4) by using data mining techniques to find good predictors of historic influenza indicators (1).

This service has yet to be extended to other countries and other diseases because access to official surveillance data is required, among other reasons. However, another Google service, Insights for Search, enables users to find and download time-series data of relative counts of arbitrary searches for a large number of countries (5). Pelat et al. have shown that a few, well-chosen searches on Google Insights provide data that closely correlate with French surveillance data for seasonal influenza, chickenpox, and gastroenteritis (3). Although Internet searches appear to be a promising tool for public health surveillance, our experience from using Google Insights in the context of pandemic (H1N1) 2009 in Singapore suggests it has important limitations.

Figure

Figure. Number of Google searches conducted for “influenza” (black lines) and “H1N1” (gray lines) compared with number of acute respiratory infections (ARI, gray bars) reported in government clinics, Singapore, 2009...



In Singapore, the recent pandemic caused an outbreak that peaked at the start of August 2009; the first confirmed importation was at the end of May and first confirmed unlinked case was at the end of June. However, the number Google searches for “influenza,” “H1N1,” “swine flu,” and similar terms (in English and Chinese), as well as symptoms associated with the disease, peaked much earlier than did the number of cases (Figure). The number of searches surged after newsworthy events but was low during the epidemic itself and had declined to about 20% of maximum search volume by the time of the actual peak, as shown by traditional surveillance. Furthermore, no discernible local maxima were observed that corresponded to the peak in case data. In contrast, alternative traditional measures of influenza incidence—prevalence of the novel strain among viral samples and general practice surveillance (6,7)—provide a consistent description of the outbreak.

This finding echoes a major point raised by Carneiro and Mylonakis (2), namely, that without adjusting for spikes driven by disease publicity rather than the disease itself, Internet searches may lose much of their value in supplementing traditional surveillance measures. Our experience is that using Google Insights to survey a disease may not work well for diseases with considerable media exposure, in particular, emerging diseases such as pandemic (H1N1) 2009 or severe acute respiratory syndrome. Such outbreaks may require the more sophisticated approach used by Flu Trends, should it be extended to other diseases and more corners of the globe. We agree with Pelat et al. (3) that Google Insights may work well for less-publicized infectious diseases. The dividing line between well-publicized and unpublicized diseases may, however, remain ambiguous. Thus, to ensure that web search data reflect disease incidence requires validation against traditional surveillance, although in that situation, the availability of corroborating traditional methods of surveillance limits the value of web-query data.

Acknowledgments
A.R.C. received research funding from the National University of Singapore.

References
1.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. PubMed DOI
2.Carneiro HA, Mylonakis E. Google Trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis. 2009;49:1557–64. PubMed DOI
3.Pelat C, Turbelin C, Bar-Hen A, Flahaut A, Valleron A-J. More diseases tracked by using Google Trends. Emerg Infect Dis. 2009;15:1327–8. PubMed DOI
4.Flu trends [cited 2010 May 25].
5.Insights for search beta [cited 2010 May 25].
6.Cutter JL, Ang LW, Lai FY, Subramony H, Ma S, James L. Outbreak of pandemic influenza A (H1N1–2009) in Singapore, May to September 2009. Ann Acad Med Singapore. 2010;39:273–82. PubMed
7.Ong JB, Chen MI-C, Cook AR, Lee HC, Lee VJ, Lin RT, et al. Real-time epidemic monitoring and forecasting of H1N1–2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PLoS ONE. 2010;5:e10036. PubMed DOI
Figure
Figure. Number of Google searches conducted for “influenza” (black lines) and “H1N1” (gray lines) compared with number of acute respiratory infections (ARI, gray bars) reported in government clinics, Singapore, 2009...

Suggested Citation for this Article
Cook AR, Chen MI-C, Lin RTP. Internet search limitations and pandemic influenza, Singapore [letter]. Emerg Infect Dis [serial on the Internet]. 2010 Oct [date cited]. Available from http://www.cdc.gov/EID/content/16/10/1647.htm

DOI: 10.3201/eid1610.100840


Comments to the Authors
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Address for correspondence: Alex R. Cook, Department of Statistics and Applied Probability, National University of Singapore, 6 Science Dr 2, Singapore 117546; email: alex.richard.cook@gmail.com

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