Hulth and Rydevik BMC Public Health 2011, 11:252 http://www.biomedcentral.com/1471-2458/11/252 the source is also better suited for diseases with a wider spread. Worth noting though, is the ability of a system like GET WELL to provide reassurance that there are no widespread negative health effects of a particular event. For example, data from GET WELL were used to investigate the development of symptoms that could be caused by ash from the volcanic eruption from Eyjafjal- lajokull during a couple of weeks in April 2010. There are a number of parameters to consider when selecting a medical web site for monitoring epidemiolo- gical trends. First of all, the representativeness of the visitors, the geographical coverage and the number of visitors should be investigated. Secondly, the accessibility of historical data as well as continuing public availability of the web site need to be ensured. A third parameter – and in practise the critical one – is the willingness of those running the web site to have a long-term coopera- tion. If there are multiple candidate web sites, using sev- eral sites could be an option, either by investigating output from different sites independently or by using the data together as input to a single surveillance sys- tem. An alternative surveillance source could be the public health departments’ own search logs, although initially the representativeness of the visitors must be examined. Conclusions In this paper, we have presented GET WELL, which is a computerized surveillance system based on anonymous logs of queries submitted to the search engine on a Swedish medical web site. The system is now in regular use at the Swedish Institute for Infectious Disease Con- trol. The web queries are a source of information which can be a valuable complement to the traditional surveil- lance. A system based on web queries is flexible in that it can be adapted to any disease; we get information on other individuals than those who seek medical care; and the data do not suffer from reporting delays. We also described a comparison between generated time series from the search data on the medical web site (Vårdguiden) with those from a general purpose search engine (Google). Web queries from a medical web site seem to be less affected by curious searchers than a gen- eral purpose search engine, and one of the investigated queries showed a substantially more interesting epide- miological pattern. For the other investigated query, the difference in search pattern between the general search engine and the search engine on the medical web site was small. We see, however, that the main advantage with not using data from Google is that full access to raw data enables statistical analyses, tailored according to the needs of the epidemiologists. We also strongly feel that public health institutes should have the full responsibility for the public health surveillance. Page 7 of 8 Moreover, collaborating with players in the public sector means that the collaboration is less dependent. There are limitations of any one source, and epide- miologists should strive at using multiple sources in their analyses. With laboratory data, for example, we suffer from reporting delays, while the sources used for syndromic surveillance usually are pre-diagnostic and thus more uncertain. By examining the output from var- ious systems, a fuller picture of the disease under inves- tigation is given. A system like GET WELL can be one important component in the daily surveillance of infec- tious diseases in that it can complement the more tradi- tional surveillance, and potentially give rise to new epidemiological insights. Acknowledgements Anette Hulth was funded by the Swedish Civil Contingencies Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Thanks to Vårdguiden for granting access to the query logs, and to Euroling for making the access feasible. Thanks to Kenneth Hebing and Stefan Vietórisz for implementing the web front-end, to Tor Johnson for technical advice, and to Tony Hällström for technical support. Thanks also to Annika Linde, Moa Rehn, and Sandra Rubinova for the collaboration on the influenza reports, and to Mikael Andersson, Yvonne Andersson, and Kjell-Olof Hedlund for their work on the norovirus reports. Finally, thanks to Hanna Merk, Sharon Kühlmann Berenzon and the reviewers for valuable comments on drafts of the paper. Authors’ contributions AH conceived of and implemented the system. GR conceived of and implemented the statistical analyses (except the norovirus surveillance). Both authors contributed to the analyses of the work, and drafted, read and approved the manuscript. 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