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      Detecting influenza epidemics using search engine query data

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          Abstract

          Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.

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          Most cited references 4

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          Strategies for containing an emerging influenza pandemic in Southeast Asia.

          Highly pathogenic H5N1 influenza A viruses are now endemic in avian populations in Southeast Asia, and human cases continue to accumulate. Although currently incapable of sustained human-to-human transmission, H5N1 represents a serious pandemic threat owing to the risk of a mutation or reassortment generating a virus with increased transmissibility. Identifying public health interventions that might be able to halt a pandemic in its earliest stages is therefore a priority. Here we use a simulation model of influenza transmission in Southeast Asia to evaluate the potential effectiveness of targeted mass prophylactic use of antiviral drugs as a containment strategy. Other interventions aimed at reducing population contact rates are also examined as reinforcements to an antiviral-based containment policy. We show that elimination of a nascent pandemic may be feasible using a combination of geographically targeted prophylaxis and social distancing measures, if the basic reproduction number of the new virus is below 1.8. We predict that a stockpile of 3 million courses of antiviral drugs should be sufficient for elimination. Policy effectiveness depends critically on how quickly clinical cases are diagnosed and the speed with which antiviral drugs can be distributed.
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            Containing pandemic influenza at the source.

            Highly pathogenic avian influenza A (subtype H5N1) is threatening to cause a human pandemic of potentially devastating proportions. We used a stochastic influenza simulation model for rural Southeast Asia to investigate the effectiveness of targeted antiviral prophylaxis, quarantine, and pre-vaccination in containing an emerging influenza strain at the source. If the basic reproductive number (R0) was below 1.60, our simulations showed that a prepared response with targeted antivirals would have a high probability of containing the disease. In that case, an antiviral agent stockpile on the order of 100,000 to 1 million courses for treatment and prophylaxis would be sufficient. If pre-vaccination occurred, then targeted antiviral prophylaxis could be effective for containing strains with an R0 as high as 2.1. Combinations of targeted antiviral prophylaxis, pre-vaccination, and quarantine could contain strains with an R(0) as high as 2.4.
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              Using internet searches for influenza surveillance.

              The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                February 2009
                February 19 2009
                February 2009
                : 457
                : 7232
                : 1012-1014
                Article
                10.1038/nature07634
                19020500
                © 2009

                http://www.springer.com/tdm

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