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Disease searches on Wikipedia could aid in forecasts of public health threats

The Los Angeles Times

Can public health experts tell that an infectious disease outbreak is imminent simply by looking at what people are searching for on Wikipedia? Yes, at least in some cases.

Researchers from Los Alamos National Laboratory were able to make extremely accurate forecasts about the spread of dengue fever in Brazil and flu in the United States, Japan, Poland and Thailand by examining three years’ worth of Wikipedia search data. They also came up with moderately successful predictions of tuberculosis outbreaks in Thailand and China, and of dengue fever’s spread in Thailand.

However, their efforts to anticipate cases of cholera, Ebola, HIV and plague by extrapolating from search data left much to be desired, according to a report published Thursday in the journal PLOS Computational Biology. But the researchers believe their general approach could still work if they use more sophisticated statistics and a more inclusive data set.

Accurate data on the spread of infectious diseases can be culled from a variety of sources. Government agencies typically get it from patient interviews and laboratory test results. Other data sources include calls to 911 lines, emergency room admissions and absences from work or school.

The problem with these methods is that they can be time-consuming and costly. By the time the numbers are crunched, an outbreak may be in full swing.

If you want to stop an outbreak before it starts — and if you want to save lives and money, you certainly do — what you need is a forecast that is accurate and timely. And so the Los Alamos researchers turned to the treasure trove that is Wikipedia.

In addition to the about 30 million articles on topics ranging from quantum foam to the First English Civil War to Kim Kardashian, Wikipedia also collects data on the approximately 850 million search requests it gets each day. In previous studies, researchers have used this publicly available data to predict ticket sales for new movies and the movement of stock prices.


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