AI tool provides more accurate flu forecasts: Study

By incorporating location data, the AI system is able to outperform other state-of-the-art forecasting methods, delivering up to an 11 per cent 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 we used a graph neural network to encode flu infections as interconnected regional clusters," said study author Yue Ning from Stevens Institute  of Technology in the US.

It 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.

"By enabling better resource allocation and public health planning, this tool will have a big impact on how we cope with influenza outbreaks," Ning added.

The research team trained their AI tool using real-world state and regional data from the US 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.

"Our model is also extremely transparent -- where other AI 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.

So far, the AI 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.

Their study was presented at the 29th ACM International Conference on Information and Knowledge Management.