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FluBreaks: early epidemic detection from Google flu trends.
J Med Internet Res. 2012 Oct 04; 14(5):e125.JM

Abstract

BACKGROUND

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.

Authors+Show Affiliations

School of Science and Engineering, Computer Science Department, Lahore University of Management Sciences, Lahore, Pakistan. fahadp@lums.edu.pkNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

23037553

Citation

Pervaiz, Fahad, et al. "FluBreaks: Early Epidemic Detection From Google Flu Trends." Journal of Medical Internet Research, vol. 14, no. 5, 2012, pp. e125.
Pervaiz F, Pervaiz M, Abdur Rehman N, et al. FluBreaks: early epidemic detection from Google flu trends. J Med Internet Res. 2012;14(5):e125.
Pervaiz, F., Pervaiz, M., Abdur Rehman, N., & Saif, U. (2012). FluBreaks: early epidemic detection from Google flu trends. Journal of Medical Internet Research, 14(5), e125. https://doi.org/10.2196/jmir.2102
Pervaiz F, et al. FluBreaks: Early Epidemic Detection From Google Flu Trends. J Med Internet Res. 2012 Oct 4;14(5):e125. PubMed PMID: 23037553.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - FluBreaks: early epidemic detection from Google flu trends. AU - Pervaiz,Fahad, AU - Pervaiz,Mansoor, AU - Abdur Rehman,Nabeel, AU - Saif,Umar, Y1 - 2012/10/04/ PY - 2012/03/08/received PY - 2012/07/10/accepted PY - 2012/05/18/revised PY - 2012/10/6/entrez PY - 2012/10/6/pubmed PY - 2013/2/12/medline SP - e125 EP - e125 JF - Journal of medical Internet research JO - J. Med. Internet Res. VL - 14 IS - 5 N2 - BACKGROUND: 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. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/23037553/FluBreaks:_early_epidemic_detection_from_Google_flu_trends_ L2 - https://www.jmir.org/2012/5/e125/ DB - PRIME DP - Unbound Medicine ER -