About Nesta

Nesta is an innovation foundation. For us, innovation means turning bold ideas into reality and changing lives for the better. We use our expertise, skills and funding in areas where there are big challenges facing society.

STAGE OF CRISIS: Preparedness, Mitigation


USE CASE: Predicting a crisis before it happens

What is the problem?

Dengue is a mosquito-borne infection and a major international public health concern, with severe dengue affecting many Asian and Latin American countries and up to 2.5 billion people worldwide. A major challenge in many dengue endemic countries is that disease surveillance is often passive and reliant on case reporting by health workers, meaning it can be difficult to ensure completeness and timeliness of data. Often dengue outbreaks have occurred or even passed their peak before they are registered by systems. Time delays and poor predictive capability mean that public health officials are missing vital opportunities for disease control and to implement a timely localised response.

Predicting outbreaks is of paramount importance as there is no treatment to prevent or stop dengue, and the WHO recommends that the vaccine only be given to persons with confirmed prior dengue virus infection, thereby leaving large swathes of the population in disease endemic countries at risk.

What is the CCI solution?

Artificial intelligence in Medical Epidemiology (AIME) is an early warning system incorporating AI to identify and forecast dengue outbreaks with a high accuracy and spatial precision. AIME leverages the intelligence of local doctors and healthcare professionals and combines this with a wide range of data sources and machine learning to predict (and therefore prevent) disease outbreak.

How is it being done?

AIME uses real-time data generated by doctors, who send in notifications of dengue cases via AIME’s subsystem (REDINT); this can be accessed by hospitals within a community/region, enabling them to immediately update and report dengue cases to the public health municipality. Case data is combined with existing datasets of variables that influence the spread of dengue — from local terrain and elevation to roofing types and thunderstorms, as well as official data about the local population and socioeconomic variables. The AIME platform combines data and uses a machine learning algorithm in order to predict, geo-locate and determine future outbreaks.

The platform enables surveillance of current disease outbreaks, by determining their exact location, and can forecast future outbreaks.

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So what?

The predictive accuracy of the AIME platform has been tested in Malaysia, Brazil and Philippines, and has been able to predict dengue outbreaks up to three months in advance and geo-locate them up to a 400 meter radius. In Malaysia, the platform predicted dengue outbreaks with an accuracy of 81 per cent. In Brazil and the Philippines the accuracy was 84 per cent.

AIME combines data sources with machine learning to improve disease surveillance and forecasting, in turn allowing clinical and public health services to move away from passive surveillance to proactive planning and response.