The authors have recently used public data available from
Wikipedia searches to model disease spread in 4 countries. They were able to
effectively model influenza in the US, Thailand, Japan and Poland and Dengue in
Brazil and Thailand. People tend to search their symptoms or research
their disease before going into a hospital if they have a suspicion of having
the disease. These trending searches can lead to predictive measurements up to
28-days before official governmental data.
The model has great strengths in
that it is very adaptable country to country, however there are several hurdles
in modeling some diseases and some countries. The model cannot yet predict the
spread of rare diseases such as Ebola, or diseases with a longer incubation
times such as HIV. Another hurdle is language, while Japanese is fairly country
specific, English is widely spoken. Yet the researchers were still able to
predict the spread of influenza in the US—they hope to receive country specific
data in the future to improve the model further. Improvements on the model could certainly
enlighten the spread of more difficult diseases in the future.
This is an exciting opportunity. To
the authors’ knowledge, other computational models of diseases are limited to
local contexts and limited to diseases, but this new model is widely
transferrable. Because Wikipedia is free and open to the public, epidemics can
be modeled in countries where official data is unavailable. Flu alone kills 3,000
to 49,000 people each year in the United States. Such a model could give us a
leg up in preparing against disease both seasonal and emerging, likely will be
an asset for public health in the near future.
-- Will St. Amant
http://www.washingtonpost.com/news/to-your-health/wp/2014/11/13/how-wikipedia-reading-habits-can-successfully-predict-the-spread-of-disease/
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