J. Med. Internet Res. - FluBreaks: early epidemic detection from Google flu trends.

Tópicos

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Resumo

CKGROUND: The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases.OBJECTIVE: To evaluate whether these trends can be used as a basis for an early warning system for epidemics.METHODS: We present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data.RESULTS: Across our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region's population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively.CONCLUSIONS: We present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.

Resumo Limpo

ckground googl flu trend servic launch track chang volum onlin search queri relat flulik symptom last year trend data produc servic shown consist relationship actual number flu report collect us center diseas control prevent cdc often identifi increas flu case week advanc cdc record howev contrari popular belief googl flu trend earli epidem detect system instead design baselin indic trend chang number diseas casesobject evalu whether trend can use basi earli warn system epidemicsmethod present first detail algorithm analysi googl flu trend can use basi build fulli autom system earli warn epidem advanc method use cdc base work present novel earli epidem detect system call flubreak dritteorgflubreak base googl flu trend data compar accuraci practic three type algorithm normal distribut algorithm poisson distribut algorithm negat binomi distribut algorithm explor relat merit method relat find chang internet penetr popul size region googl flu trend provid dataresult across perform metric percentag trueposit rtp percentag falseposit rfp percentag overlap ot percentag earli alarm ea poisson negat binomialbas algorithm perform better except rfp poissonbas algorithm averag valu rtp rfp ot ea respect wherea negat binomialbas algorithm averag valu rtp rfp ot ea respect moreov ea also affect region popul size region larger popul region higher valu ea region smallest popul negat binomi poissonbas algorithm differ averag negat binomi poissonbas algorithm respectivelyconclus present first detail compar analysi popular earli epidem detect algorithm googl flu trend data note realiz opportun requir move beyond cumul sum histor limit methodbas normal distribut approach tradit employ cdc negat binomi poissonbas algorithm deal potenti noisi search queri data region vari popul internet penetr base work develop flubreak earli warn system flu epidem use googl flu trend

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