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Using Artificial Intelligence to Predict the Next Zika/Dengue Outbreak

Lisa Jones | April 13, 2017

“Right now, we have a sick care system,” explains Rainier Mallol. “What we need is a healthcare system.”

At just 25 years old, Rainer has an impressive résumé. Out of 18,000 nominees, last year he was named one of 18 UN Young Leaders tasked with championing youth-led solutions to the Sustainable Development Goals (SDGs).

A year earlier, in 2015, Rainer was selected to attend Singularity University, a Google-funded program hosted at NASA Research Park in Silicon Valley. The program brings together accomplished experts in academics, business, and government with top graduate and post graduate students and entrepreneurial leaders from around the globe to collaborate in pursuit of solving challenges that affect at least 1 billion people.

It was this experience that led Rainier to his most important accomplishment to date—helping to safeguard the health of over 10 million people.

Along with a team he met at Singularity University, Rainier co-founded Artificial Intelligence in Medical Epidemiology (AIME), a venture that merges epidemiology and medical expertise with machine learning, a branch of artificial intelligence.

“We’re not a tech company—we’re an epidemiology company that leverages the power of technology” says Rainier.

Their first success? Developing an algorithm that can predict the location of the next Dengue, Zika, or Chikungunya outbreak up to three months in advance—with over 86% accuracy.

The model is so effective that AIME secured contracts from the local health departments of Rio de Janeiro, Manila, and Kuala Lumpur to help them target prevention efforts in 2015 and 2016.

“Public health invests a lot in broad fumigation and health campaigns that aren’t as effective as targeted efforts,” explains Rainier.

To solve this problem, AIME helps health departments zero in on the areas likely to be most affected, allowing them to invest resources more strategically and develop solutions that are customized to the communities that need them most.

“The idea that governments should be slow is one that many believe blindly. Why can’t governments use data-driven systems to serve its citizens more efficiently?” asks Rainier in his Medium article, Why Data is the New Oil.

While AIME’s success in transforming three cities’ approaches to preventing mosquito-transmitted diseases is monumental, Rainier sees it as just the beginning—a proof of concept.

“We’re contributing to SDG #3: Ensure healthy lives and promote well-being for all” says Rainier. “What we have is a machine that can understand the way diseases behave, the way the climate change behaves, the way that we behave.”

Rainier envisions applying the AIME model to a broad array of urgent public health challenges in the future—from curbing the transmission of diseases like AIDS to containing sudden crises such as the 2013-2016 Ebola outbreak.

“My vision is a world where we can prevent the spread of any disease,” says Rainier. “A world where we can not only improve medications, but improve our lives.”