The spatial distribution of Plasmodium falciparum malaria endemicity in 2010.
You are stuck in bed with a snotty nose and flu. You grab your smart phone and use 140 characters to declare to your Twitter followers: “Feel awful. Fever burning up my bed #sickday”. Unbeknownst to you, your tweet could be part of a global effort to map infectious diseases.
Tweets have been shown to be extremely useful in predicting outbreaks of disease. In the US, studies have found that analysing trends on Twitter could indicate an outbreak of flu two or three weeks before the Centres for Disease Control and Prediction announce a problem. It does, however, come with a note of caution and a warning about common sense. In one study they found a massive spike monitoring the symptom word “fever”. Closer inspection revealed the tweets were a meme about pop star Justin Bieber (“Bieber Fever”).
Tweets are among the innovative information-gathering methods David Pigott and a team fromMalaria Atlas Project (MAP) have recommended in a review of global mapping of infectious diseases.
Accurate maps make it possible to plan treatment strategies and infrastructure such as clinics. They also help researchers to know when a disease has spread to a new area and to predict how it may increase in places where they have no solid information.
However, the review found a massive deficit in accurate mapping. Of 355 diseases recorded by the Global Infectious Diseases and Epidemiology Network, 174 could do with accurate and up-to-date maps of where these diseases are occurring. But only seven had been mapped. Moreover, many of the disease maps were based on sketchy data, such as anecdotal evidence of where a disease is thought to occur.
The traditional way of getting information is to sift through reams of journals and grey literature. However, the worldwide web is offering new, innovative ways of measuring disease occurrence. The data gathered online is recent, and can be gained from outlying areas where academic research is less prolific. One example comes from a study published this month in Nature. In it, the MAP team and other researchers measured the global burden of dengue fever. Among their sources for new cases of infection was Health Map, an online tool that trawls through online news stories. Data gathered from Health Map accounted for between 20 to 30 per cent of the total data in the study.
The team is also borrowing tools from other fields of research. For example the genetic sequence database, GenBank, is widely used by microbiologists and the amount of data it holds is growing exponentially, doubling every 18months. MAP believe it might be an untapped resource for their own work where sequences are stored with geographic locations.
For the MAP team, malaria has been the key focus for several years, But they are actively moving beyond this and trying to tackle those 173 other map-needy diseases. “If there is going to a lot of money invested in specific areas of disease, you need a map to know where exactly this disease is,” says Pigott. The ultimate goal is to have a sophisticated online map that could automatically collect information from the web to predict where infectious diseases are going to strike next.
Reference
Hay S.I., Battle K.E., Pigott D.M., Smith D.L., Moyes C.L., Bhatt S., Brownstein J.S., Collier N., Myers M.F. & George D.B. & (2013). Global mapping of infectious disease., Philosophical transactions of the Royal Society of London. Series B, Biological sciences, PMID: 23382431
Theresa Taylor
Theresa is an intern at the Wellcome Trust.
The Malaria Atlas Project is supported by the Wellcome Trust.
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