The Parable of Google Flu: Traps in Big Data Analysis

@article{Lazer2014ThePO,
  title={The Parable of Google Flu: Traps in Big Data Analysis},
  author={David Lazer and Ryan Kennedy and Gary King and Alessandro Vespignani},
  journal={Science},
  year={2014},
  volume={343},
  pages={1203 - 1205}
}
Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data. In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from… 
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2,005 Citations

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Big data diagnostics.

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References

SHOWING 1-10 OF 64 REFERENCES

Detecting influenza epidemics using search engine query data

A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH0.1, especially during the summer months, and changes in health-seeking behavior may have played a part.

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

It is demonstrated that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza.

Flu Near You: An Online Self-reported Influenza Surveillance System in the USA

It was found that it is possible to engage users in a symptom self-reporting system and augment information about influenza for the nation, and increased uptake would increase the value of the system for the public and public health professionals.

CRITICAL QUESTIONS FOR BIG DATA

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the…

Beating the news using social media: the case study of American Idol

Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events.

Epidemiology of seasonal influenza: use of surveillance data and statistical models to estimate the burden of disease.

Improvements in national influenza surveillance systems will be needed to collect and analyze data in a timely manner during the next pandemic.

Forecasting seasonal outbreaks of influenza

It is indicated that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak, and confidence in those predictions can be inferred from the spread of the forecast ensemble.

Real-time epidemic forecasting for pandemic influenza

Using epidemiological data collected during the early stages of an outbreak, it is shown how the timing of the maximum prevalence of the pandemic wave, along with its amplitude and duration, might be predicted by fitting a mass-action epidemic model to the surveillance data by standard regression analysis.

Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore

The real-time surveillance system is able to show the progress of an epidemic and indicates when the peak is reached and can be used to form forecasts, including how soon the epidemic wave will end and when a second wave will appear if at all.
...