When Google got flu wrong
nature.com
nature.com
http://www.nature.com/polopoly_fs/7.8976.1360689365!/image/F...
The data is there, it's correct, it accurately measures how much people are talking about the flu. The fact that it's lined up with past flu seasons is simply a good sign of correlation in the past.
The area between the inflated google trend and the real number of cases is the amount of hype. It's been talked about in the media and online like it was an epidemic of 10 times the size that it actually was, and this likely had a positive effect on vaccination rates and conscientiousness.
The google data is still extremely useful as a measure of our collective attention, but the article really fails to give it credit and seems to think that it's failed somehow. It could be further refined, sure, but it's still extremely useful and extremely true. It shows that this year, the flu went viral, and the attention was amplified.
Maybe we need a Google Flu Tracker Tracker? (http://3.bp.blogspot.com/_Otk-knCm-nw/ShT1qAUsWiI/AAAAAAAAAQ...)
[1]http://gizmodo.com/5974671/you-will-not-be-able-to-escape-th...
Google doesn't claim that GFT replaces epidemiologists, and epidemiologists agree. And the article didn't say otherwise, if you actually read it.
Google, and the CDC, and the article, agree that Google returns data much faster than the CDC does, which is a service of some value. Similarly, Google can sometimes give finer-grained geographic resolution.
Everyone agrees that it would be nice to know about flu trends in countries that don't have good epidemiological analysis. GFT helps with that.
The article isn't saying that GFT is useless, nor spreading FUD, nor is it anti-strange-new-technology. Nor are the epidemiologists saying that. The article is pointing out that GFT this year didn't do as well as it has most years since its inception, and that the modest-but-proud claims of the GFT team are pretty much exactly proportional to GFT's efficacy, rather than being false modesty.
Note that there are other Google Trends published, many with similar benefits and faults. For example, Google's guesses about unemployment rates are released in real time, but are presumably accurate than government reports. That's a tradeoff.
tl;dr: GFT is useful but not miraculous, and if you read the article carefully, the article says that.
The main (and interesting) point is that heavy media coverage of flu caused people who weren't ill to search for it, which Google's algorithm misinterpreted.
I think traditional researchers should scrutinize new-tech methods applied by Google and others, as their domain expertise is valuable in finding mistakes/discounting assumptions by an algorithm.
But -- and I don't speak from expertise -- I'm thinking that the data that traditional researchers use for these kinds of assessments, has usually been very structured and dependent on the reliability and frequency of official reports. Google and machine learning brings a whole new capability of interpreting unstructured, seemingly unrelated data, that may consist of a lot of noise, but also contains insights that were otherwise impossible to get through the traditional research and data collecting process.