Hulth and Rydevik BMC Public Health 2011, 11:252 http://www.biomedcentral.com/1471-2458/11/252 Page 6 of 8 Figure 6 Data on “vomit”, 2007-2010. Queries on vomit submitted to Google according to Google Insights for Search and to Vårdguiden, from week 1, 2007 to week 4, 2010. The Google data are normalized and scaled. The Vårdguiden data are normalized. number of queries containing the stem kräk (vomit) submitted to Vårdguiden during the same period. As can be seen in the lowest graph in Figure 6, the seasonal pattern of vomiting is then very pronounced. Since the pattern from *kräk* follows the seasonality of the winter vomiting disease, it has a higher epidemic relevance [15]. Discussion The traditional surveillance at SMI is mainly based on reports from clinicians and laboratories. GET WELL is developed to serve as a complement to this traditional surveillance. The output from GET WELL can be used to identify epidemiological problems, which then can be investigated further by gathering more detailed informa- tion – such as geographical, age or serotype distribution – through other sources. GET WELL can also be used to confirm or contradict signals from other surveillance systems, and the time series can be used for regular monitoring. Once relevant search patterns have been identified, evaluations tailored for the disease in ques- tion, taking into account the available traditional surveil- lance are required. The epidemiologists are instrumental in these evaluations. Influenza and norovirus infections are currently monitored on a weekly basis by automatic dispatches from GET WELL. The web query-based sur- veillance for these two diseases has been evaluated with respect to the traditional surveillance [7,15]. From per- sonal communications, the epidemiologists at SMI in charge of the surveillance of these diseases see great value in the output from the system. From the comparison between search patterns from Vårdguiden and those obtained from Google Insights for Search, we can see qualitative differences. For exam- ple, it seems that substantial media attention affects Google more than it affects the medical web site. Also, an epidemiologically interesting seasonal pattern was found in the Vårdguiden data for the query on vomit, while such a pattern was hardly discerned in the Google data. It could be that those who search for information out of curiosity rather than illness obscure the epidemiologi- cal link between search and disease. One evidence that the specificity is higher in the Vårdguiden data than in the Google data is that the second most common search phrase for vomiting on Google for the investigated per- iod and country is vomiting dog, while this query has occurred only five times in total on Vårdguiden during this period. In the context of human epidemiological surveillance, search terms refereeing to animal illness are rarely relevant. There are several advantages with having access to the raw data. For tailored analyses, we have total control of the data and can transform it, if needed, to the required format. We can also customize the matching process to the investigated language, when counting the number of queries submitted to the web site. As mentioned, Swed- ish is rich in compounding, and by extracting all queries matching a particular stem, we may get a search pattern which is of greater value in its epidemiological information. On the other hand, there are other advantages with Google. For example, as Google stores IP addresses, regional information can be provided. (The Vårdguiden query logs are anonymous, which can be a disadvantage from the point of view of surveillance, but from the point of view of the individual who is seeking informa- tion on personal health problems, we see it as crucial for the question of personal integrity.) Also, with Google Insights for Search, international comparisons can quickly be made. In addition, data are easily accessible. For a discussion on how Google trends can be used for infectious disease surveillance, see [16,17]. Also, Pelat et al have performed analyses on three diseases on French Google search data [18]. GET WELL is currently only used for monitoring known diagnoses and symptoms. Potentially, web query data could be used as a source for early detection of outbreaks or other health hazards. This type of data is, however, inherently noisy, which means that the number of queries of a particular symptom would need to rise considerably if the system was not to overwhelm a user by false alarms at other times. For the reason of noise,