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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 U.S., Japan, Poland and Thailand by examining three years worth of Wikipedia search data. They also came up with moderately success predictions of tuberculosis outbreaks in Thailand and China, and of dengue fevers 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 both 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.

When it comes to health, people have found correlations between interest in certain health topics on Wikipedia and sales of medications. Others have linked searches for flu-related topics by American Wikipedia users to actual flu spread in the U.S.

Five members of the LANLs Defense Systems and Analysis Division thought they could do more. Their goal was to get a read on current and future trends not just for flu in the U.S. but for several diseases in several countries. Ideally, they hoped to come up with a model that could be trained with data from a place where its available and then applied to another place where it wasnt.

The researchers decided to focus on seven diseases (cholera, dengue fever, Ebola, HIV/AIDS, influenza, plague and tuberculosis) in nine countries (Brazil, China, Haiti, Japan, Norway, Poland, Thailand, Uganda and the U.S.). They mixed and matched to get models for 14 location-disease contexts.

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Scientists use Wikipedia search data to forecast spread of flu

Wikipedia page views could, in the future, become an important tool in predicting disease outbreaks, according to the findings of a new studypublished in the journal PLOS Computational Biology. The research, carried out by a group of data scientists from the Los Alamos National Laboratory in New Mexico, argued that Wikipedia traffic data could also be used to estimatethe current rates of disease outbreaks across the world.

The team of scientists tracked the progress of seven diseases across 11 countries -- using language as an approximate measure for peoples locations -- between 2010 and 2013, and compared page views on Wikipedia articles about those diseases with data obtained from health ministries. Based on this comparison, the researchers found that, in eight out of 14 cases, there was a clear increase in page views nearly a month before an official declaration of an outbreak.

Using this technique, they were able to predict influenza outbreaks in the U.S., Poland, Japan and Thailand, the spread of dengue in Brazil, and a spike in the number of tuberculosis cases in Thailand.

The research was based on the theory that people tend to search online for symptoms of the disease they suspect they have before being officially diagnosed. The researchers claimed that Wikipedia is the best bet to create an Internet-based model to predict outbreaks because data on Wikipedia page views are publicly available.

Using simple statistical techniques, our proof-of-concept experiments suggest that these data are effective for predicting the present, as well as forecasting up to the 28-day limit of our tests. Our results also suggest that these models can be used even in places with no official data upon which to build models, the researchers said, in the paper, adding that the new method could overcome key gaps in existing traditional and internet-based techniques.

Traditional disease surveillance techniques involve collecting data from laboratory tests and tracking the number of visits to health care facilities. The researchers claimed that while these techniques are accurate, they are also slow and expensive.

However, the Wikipedia-based model was not successful in predicting the spread of slow-progressing diseases like HIV/AIDS, according to the paper. Moreover, several scientists also questioned the extent to which the model could be used in areas with poor Internet penetration, or in relation to poorly understood diseases.

I'm not sure how much Wikipedia is used in Africa, Heidi Larson, an anthropologist from the London School of Hygiene and Tropical Medicine,told BBC. For issues like Ebola, I don't think people at the beginning of the outbreak in West Africa would have (been searching for it), because they wouldn't have had it (Ebola) before.

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Wikipedia-Based Tracking Model Could Predict Disease Outbreaks: Study