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Artificial intelligence predictor of dengue outbreaks being trialled in Malaysia

last modified May 30, 2018 06:56 PM
The machine-learning system harnesses hundreds of parameters ranging from wind speed to local roof architecture to try and predict where the next outbreak will be. It then advises responders on the intervention likely to be most effective in that particular area, such as fogging or removing water pools.

Dengue is a mosquito-borne viral infection that has grown rapidly in recent decades, with half the world’s population at risk. Cases of infection number in their millions every year, with half a million hospitalised with severe dengue, of whom about 13,000 lose their lives to the disease.

In Asia, vector control costs over US$300 million annually, while South America spends US 1 billion to control dengue, according to Dhesi Raja, of the Institute for Medical Research Malaysia, who co-invented the system with Rainier Mallol, selected by the UN as a Young Leader for the Sustainable Development Goals.

The system is known as AIME (Artificial Intelligence in Medical Epidemiology). As doctors in the state send in notifications of dengue cases, they feed automatically into the system which then searches through over 90 databases for 276 variables that influence its spread — from local terrain and elevation to roofing types, thunderstorms, water accumulation and population density.

From these, Raja says it deduces where the next outbreaks will be within a 400-metre radius.

In historic comparisons of AIME prediction and actual outbreaks the system has shown 81-84% accuracy.


For more details see the article by Aisling Irvin on SciDevNet

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