All Title Author
Keywords Abstract

PLOS ONE  2009 

Web Queries as a Source for Syndromic Surveillance

DOI: 10.1371/journal.pone.0004378

Full-Text   Cite this paper   Add to My Lib


In the field of syndromic surveillance, various sources are exploited for outbreak detection, monitoring and prediction. This paper describes a study on queries submitted to a medical web site, with influenza as a case study. The hypothesis of the work was that queries on influenza and influenza-like illness would provide a basis for the estimation of the timing of the peak and the intensity of the yearly influenza outbreaks that would be as good as the existing laboratory and sentinel surveillance. We calculated the occurrence of various queries related to influenza from search logs submitted to a Swedish medical web site for two influenza seasons. These figures were subsequently used to generate two models, one to estimate the number of laboratory verified influenza cases and one to estimate the proportion of patients with influenza-like illness reported by selected General Practitioners in Sweden. We applied an approach designed for highly correlated data, partial least squares regression. In our work, we found that certain web queries on influenza follow the same pattern as that obtained by the two other surveillance systems for influenza epidemics, and that they have equal power for the estimation of the influenza burden in society. Web queries give a unique access to ill individuals who are not (yet) seeking care. This paper shows the potential of web queries as an accurate, cheap and labour extensive source for syndromic surveillance.


[1]  Lombardo JS, Burkom H, Pavlin J (2004) ESSENCE II and the framework for evaluating syndromic surveillance systems. MMWR Morb Mortal Wkly Rep 53: Suppl159–65.
[2]  Hogan WR, Tsui F-C, Ivanov O, Gesteland PH, Grannis S, et al. (2003) Detection of pediatric respiratory and diarrheal outbreaks from sales of over-the-counter electrolyte products. J Am Med Inform Assoc 10(6): 555–62.
[3]  Das D, Metzger K, Heffernan R, Balter S, Weiss D, et al. (2005) Monitoring over-the-counter medication sales for early detection of disease outbreaks — New York City. MMWR Morb Mortal Wkly Rep 54: Suppl41–46.
[4]  Mostashari F, Fine A, Das D, Adams J, Layton M (2003) Use of ambulance dispatch data as an early warning system for communitywide influenza like illness, New York City. J Urban Health, 80: i43–i49.
[5]  Bork KH, Klein BM, M?lbak K, Trautner S, Pedersen UB, et al. (2006) Surveillance of ambulance dispatch data as a tool for early warning. Euro Surveill 11(12): 229–233.
[6]  Heffernan R, Mostashari F, Das D, Karpati A, Kulldorff M, et al. (2004) Syndromic surveillance in public health practice, New York City. Emerg Infect Dis 10(5): 858–864.
[7]  Chapman WW, Christensen LM, Wagner MM, Haug PJ, Ivanov O, et al. (2005) Classifying free-text triage chief complaints into syndromic categories with natural language processing. Artif Intell Med 33(1): 31–40.
[8]  Josseran L, Nicolau J, Caillère N, Astagneau P, Brücker G (2006) Syndromic surveillance based on emergency department activity and crude mortality: two examples. Euro Surveill 11 (12): 225–229.
[9]  Fienberg SE, Shmueli G (2005) Statistical issues and challenges associated with rapid detection of bio-terrorist attacks. Stat Med 24: 513–529.
[10]  Johnson HA, Wagner MM, Hogan WR, Chapman W, Olszewski RT, et al. (2004) Analysis of web access logs for surveillance of influenza. Medinfo 11: 1202–1206.
[11]  Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 2008 Nov 19 (Epub ahead of print).
[12]  Polgreen PM, Chen Y, Pennock DM, Nelson FD (2008) Using internet searches for influenza surveillance. Clin Infect Dis 47: 1443–1448.
[13]  Smith G, Cooper D, Loveridge F, Chinemana F, Gerard E, et al. (2006) A national syndromic surveillance system for England and Wales using calls to a telephone helpline. Euro Surveill 11(10–12): 220–224.
[14]  Rolland E, Moore KM, Robinson VA, McGuinness D (2006) Using Ontario's “Telehealth” health telephone helpline as an early-warning system: A study protocol. BMC Health Serv Res 6: 10.
[15]  Jormanainen V, Jousimaa J, Kunnamo I, Ruutu P (2001) Physicians' database searches as a tool for early detection of epidemics. Emerg Infect Dis 7(3): 474–476.
[16]  Influenza annual report 2006–2007. The National Influenza Reference Centre, Swedish Institute for Infectious Disease Control (SMI).
[17]  Andersson E, Kühlmann-Berenzon S, Linde A, Schi?ler L, Rubinova S, Frisén M (2008) Predictions by early indicators of the time and height of the peaks of yearly influenza outbreaks in Sweden. Scand J Public Health 36(5): 475–482.
[18]  V?rdguiden Labs (2008) Vad tycker anv?ndarna om
[19]  Wehrens R, Mevik B-H (2007) The PLS Package: Principal Component and Partial Least Squares Regression in R. JSS 18(2):
[20]  Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi- and Megavariate Data Analysis. Umetrics AB.
[21]  Wehrens R, Mevik B-H (2007) PLS: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). R package version 2.1–0.
[22]  R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
[23]  Armitage P, Colton T (2005) Encyclopedia of Biostatistics. 2: 1323–1324.
[24]  Zeng X, Wagner M (2002) Modeling the effects of epidemics on routinely collected data. J Am Med Inform Assoc 9: 17–22.


comments powered by Disqus