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FluBreaks: Early Epidemic Detection from Google Flu Trends

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...

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Autores principales: Pervaiz, Fahad, Pervaiz, Mansoor, Abdur Rehman, Nabeel, Saif, Umar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Gunther Eysenbach 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510767/
https://www.ncbi.nlm.nih.gov/pubmed/23037553
http://dx.doi.org/10.2196/jmir.2102
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author Pervaiz, Fahad
Pervaiz, Mansoor
Abdur Rehman, Nabeel
Saif, Umar
author_facet Pervaiz, Fahad
Pervaiz, Mansoor
Abdur Rehman, Nabeel
Saif, Umar
author_sort Pervaiz, Fahad
collection PubMed
description 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.
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spelling pubmed-35107672012-12-28 FluBreaks: Early Epidemic Detection from Google Flu Trends Pervaiz, Fahad Pervaiz, Mansoor Abdur Rehman, Nabeel Saif, Umar J Med Internet Res Original Paper 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. Gunther Eysenbach 2012-10-04 /pmc/articles/PMC3510767/ /pubmed/23037553 http://dx.doi.org/10.2196/jmir.2102 Text en ©Fahad Pervaiz, Mansoor Pervaiz, Nabeel Abdur Rehman, Umar Saif. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.10.2012. http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Pervaiz, Fahad
Pervaiz, Mansoor
Abdur Rehman, Nabeel
Saif, Umar
FluBreaks: Early Epidemic Detection from Google Flu Trends
title FluBreaks: Early Epidemic Detection from Google Flu Trends
title_full FluBreaks: Early Epidemic Detection from Google Flu Trends
title_fullStr FluBreaks: Early Epidemic Detection from Google Flu Trends
title_full_unstemmed FluBreaks: Early Epidemic Detection from Google Flu Trends
title_short FluBreaks: Early Epidemic Detection from Google Flu Trends
title_sort flubreaks: early epidemic detection from google flu trends
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510767/
https://www.ncbi.nlm.nih.gov/pubmed/23037553
http://dx.doi.org/10.2196/jmir.2102
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