A study published in the journal Nature Communications, shows that the approach, called ARGONet, makes more accurate predictions than the earlier high-performing forecasting approach, ARGO.
"Timely and reliable methodologies for tracking influenza activity across locations can help public health officials mitigate epidemic outbreaks and may improve communication with the public to raise awareness of potential risks," said Mauricio Santillana, from Computational Health Informatics Program (CHIP) at Boston Children's Hospital in the US.
The first model, ARGO (AutoRegression with General Online information), leverages information from electronic health records, flu-related Google searches and historical flu activity in a given location.
In the study, ARGO alone outperformed Google Flu Trends, the previous forecasting system that operated from 2008 to 2015.
To improve accuracy, ARGONet adds a second model, which draws on spatial-temporal patterns of flu spread in neighboring areas.
"It exploits the fact that the presence of flu in nearby locations may increase the risk of experiencing a disease outbreak at a given location," said Santillana, who is also an assistant professor at Harvard Medical School.
"The system continuously evaluates the predictive power of each independent method and recalibrates how this information should be used to produce improved flu estimates," said Santillana.
Researchers believe their approach will set a foundation for "precision public health" in infectious diseases.
"We think our models will become more accurate over time as more online search volumes are collected and as more healthcare providers incorporate cloud-based electronic health records," said Fred Lu, a CHIP investigator.
(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)