Researchers at Stevens Institute of Technology uses location data to provide robust longer-term insights on flu outbreaks
From
Stevens Institute of Technology
November
2, 2020 -- Predicting influenza outbreaks just got a little easier, thanks to a
new A.I.-powered forecasting tool developed by researchers at Stevens Institute
of Technology.
By incorporating location data, the A.I.
system is able to outperform other state-of-the-art forecasting methods,
delivering up to an 11% increase in accuracy and predicting influenza outbreaks
up to 15 weeks in advance.
Past forecasting tools have sought to
spot patterns by studying the way infection rates change over time but Yue
Ning, who led the work at Stevens, and her team used a graph neural network to
encode flu infections as interconnected regional clusters. That allows their
algorithm to tease out patterns in the way influenza infections flow from one
region to another, and also to use patterns spotted in one region to inform its
predictions in other locations.
“Capturing the interplay of space and
time lets our mechanism identify hidden patterns and predict influenza
outbreaks more accurately than ever before,” said Ning, an associate professor
of computer science. “By enabling better resource allocation and public health
planning, this tool will have a big impact on how we cope with influenza
outbreaks.”
Ning and her team trained their A.I.
tool using real-world state and regional data from the U.S. and Japan, then
tested its forecasts against historical flu data. Other models can use past
data to forecast flu outbreaks a week or two in advance, but incorporating
location data allows far more robust predictions over a period of several
months. Their work is reported in the Oct. 19 – 23 Proceedings of the
29th ACM International Conference on Information and Knowledge Management.
“Our model is also extremely transparent
— where other A.I. forecasts use ‘black box’ algorithms, we’re able to
explain why our system has made specific predictions, and how
it thinks outbreaks in different locations are impacting one another,” Ning
explained.
In the future, similar techniques could
also be used to predict waves of COVID-19 infections. Since COVID-19 is a novel
virus, there’s no historical data with which to train an A.I. algorithm; still,
Ning pointed out, vast amounts of location-coded COVID-19 data are now being
collected on a daily basis. “That could allow us to train algorithms more
quickly as we continue to study the COVID-19 pandemic,” Ning said.
Ning is now working to improve her
influenza-forecasting algorithm by incorporating new data sources. One key
challenge is figuring out how to account for public health interventions such
as vaccination education, mask-wearing and social distancing. “It’s
complicated, because health policies are enacted in response to outbreak
severity, but also shape the course of those outbreaks,” Ning explained. “We
need more research to learn about how health policies and pandemics interact.”
Another challenge is identifying which
data genuinely predicts flu outbreaks, and which is just noise. Ning’s team found
that flight traffic patterns don’t usefully predict regional flu outbreaks, for
instance, but that weather data was more promising. “We’re also constrained by
the information that’s publicly available,” Ning said. “Having location-coded
data on vaccination rates would be very helpful, but sourcing that information
isn’t easy.”
So far, the A.I. tool hasn’t been used
in real-world health planning, but Ning said that it’s just a matter of time
until hospitals and policymakers begin using A.I. algorithms to deliver more
robust responses to flu outbreaks. “Our algorithm will keep learning and
improving as we collect new data, allowing us to deliver even more accurate
long-term predictions,” Ning said. “As we work to cope with future pandemics,
these technologies will have a big impact.”
https://www.stevens.edu/news/ai-tool-provides-more-accurate-flu-forecasts
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