The second problem was that it wasn’t clear how the predictions
were actually made. GFT was essentially an opaque machine;
search data went in one end and predictions came out the other.
Google didn’t make the raw data or methods available to the wider
research community, so it wasn’t possible for others to pick apart the
analysis and work out why the algorithm performed well in some
situations but badly in others.
Then there’s the final – and perhaps biggest – issue with GFT: it
didn’t seem that ambitious. We get flu epidemics each winter
because the virus evolves, making current vaccines less effective.
Similarly, the main reason governments are so worried about a
future pandemic flu virus is that we won’t have an effective vaccine
against the new strain. In the event of a pandemic, it would take six
months to develop one,[14] by which time the virus will have spread
widely. To predict the shape of flu outbreaks, we need a better
understanding of how viruses evolve, how people interact, and how
populations build immunity.[15] Faced with this hugely challenging
situation, GFT merely aimed to report flu activity a week or so earlier
than it would have been otherwise. It was an interesting idea in
terms of data analysis, but not a revolutionary one when it comes to
tackling outbreaks.
This is a common pitfall when researchers or companies talk
about applying large datasets to wider aspects of life. The tendency
is to assume that, because there is so much data, there must be
other important questions it can answer. In effect, it becomes a
solution in search of a problem.
I 2016, epidemiologist Caroline Buckee attended a tech
fundraising event, pitching her work to Silicon Valley insiders.
Buckee has a lot of experience of using technology to study
outbreaks. In recent years, she has worked on several studies using
GPS data to investigate malaria transmission. But she is also aware
that such technology has its limitations. During the fundraising event,
she became frustrated by the prevailing attitude that with enough
money and coders, companies could solve the world’s health