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