The Zika virus that ravaged its way through South America, and up into
parts of Florida and Texas, in 2016 had a devastating social and
economic impact. The virus was linked to birth defects in thousands of Brazilian
newborns, whose mothers were infected while pregnant.
World Health Organization estimates suggest up to four million people were
infected in Latin America and the Caribbean by early 2017. Other figures
argue that up to 117 million
people,
including 1.5 million pregnant women, worldwide were infected by the
mosquito-borne disease.
According to the United Nations, the epidemic saw nations incurring huge
direct and indirect costs of up to US$18 billion over three
years;
tor those suffering the resultant microcephaly and Guillain-Barré
syndrome, the cost will be, of course, far greater — children born with
microcephaly face a 20 percent probability of dying in their first year, with a
life expectancy of just 35 years beyond their first 12 months.
Zika is not alone in striking fear across nations and controlling the spread of
disease can cost a fortune. In Asia, as much as US$307 million year is
spent on controlling vector-borne disease. In South America, close to US$1
billion dollars is spent controlling Zika and Dengue.
Such statistics drove data scientist Dhesi Raja and his computer engineer
co-founder, Rainier Mallol, to develop a solution that would drastically
change the way countries and their health practitioners deal with deadly
outbreaks of disease.
The two met during their graduate studies at NASA in California. They both
obtained a scholarship with Google for a graduate studies program, during
which they came up with the idea of revolutionizing public health and
disease-control using artificial intelligence.
“Existing work related to the control of infectious diseases is passive and
reactive — and the analysis of outbreak data is currently relying on statistical
methods,” Mallol says.
He explains that available Zika and Dengue data are limited to time, location
and accumulated cases. Other important details — such as weather, population
density, elevation, vegetation and geographical variables that could be crucial
to disease outbreak — are just not monitored or predicted in a dynamic or
real-time manner.
"While we have a number of vector control tools available in combatting Zika and
Dengue, their effective deployment relies on accurately interpreting available
data to identify the exact spot where and when such activity should take place,”
he says. “Such interpretation is heavily skill-dependent — and that made us
realize that there is a need to develop a smart machine, driven by artificial
intelligence to help predict and control deadly outbreaks.”
The result of their work is the AIME (Artificial
Intelligence in Medical Epidemiology) platform, a medtech app that is able to
predict when and where such diseases will hit, up to three months in advance —
in fact, it can geo-locate them to within a 400-metre radius with an accuracy of
86.37 percent, in real time.
Image credit: AIME/Instagram
AIME’s initial Dengue Outbreak Prediction platform is the result of
two-and-a-half years of epidemiological research, and a few months of
machine-learning analysis. The solution incorporates a huge amount of
epidemiology, weather and geographical data — as well as data on vegetation,
population density and previous outbreaks — and mixes it with machine-learning
capabilities in order to predict, geo-locate and determine future outbreaks.
In effect, AIME optimizes data-driven decision-making within public health
organisations. This is especially effective in poorer and more remote areas,
where expertise for epidemiology data interpretation is limited, at best. The
technology is said to cut the time it takes for health personnel to analyse data
by 65 percent.
It is still early days for the technology being widely used, but the company
assisted Brazilian NGO VivaRio during the
2016 Rio Olympics to highlight potential Zika and Dengue hotspots; and it
has also been deployed in the state of Penang, Malaysia, where since early
2018, cases of Dengue have fallen by 42 percent.
“With our platform, we can aid in the prevention of diseases, with the ultimate
goal of removing the chance of epidemics and eliminating viruses,” Mallol adds.
“Predicting Dengue is just our first step. Eventually, we will expand to other
epidemic diseases such as Tuberculosis, Malaria and even HIV/AIDS.
“We are not sure if our action will save lives, but we are certainly sure
inaction kills.”
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Published Jul 15, 2019 8am EDT / 5am PDT / 1pm BST / 2pm CEST