title: Web queries as a source for syndromic surveillance.
abstract: 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.
authors.affiliation: Department of Epidemiology, Swedish Institute for Infectious Disease Control, Solna, Sweden. anette.hulth@smi.se
authors.forename: Anette
authors.initials: A
authors.lastname: Hulth
authors.forename: Gustaf
authors.initials: G
authors.lastname: Rydevik
authors.forename: Annika
authors.initials: A
authors.lastname: Linde
mesh_terms: D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet; D015203:Reproducibility of Results; D018571:Sentinel Surveillance; D013997:Time Factors