Artificial intelligence techniques have inspired a new COVID-19 forecasting model provide timely information at a more localized level.
The researchers say that officials and anyone in the public can use in their decision-making processes.
“We are all overwhelmed by the data, most of which is provided at national and state levels,” says Xifeng Yan, an associate professor of and chair in computer science at the College of Engineering at the University of California, Santa Barbara.
“Parents are more interested in what is happening in their school district and if it’s safe for their kids to go to school in the fall,” Yan says. “However, there are very few websites providing that information. We aim to provide forecasting and explanations at a localized level with data that is more useful for residents and decision makers.”
“The challenges of making sense of messy data are precisely the type of problems that we deal with every day as computer scientists working in AI and machine learning,” says Yu-Xiang Wang, an assistant professor of and chair in computer science. “We are compelled to lend our expertise to help communities make informed decisions.”
'Transforming' COVID-19 Forecasting
Yan and Wang developed an innovative forecasting algorithm based on a deep learning model called Transformer. The model is driven by an attention mechanism that intuitively learns how to forecast by learning what time period in the past to look at and what data is the most important and relevant.
“If we are trying to forecast for a specific region, like Santa Barbara County, our algorithm compares the growth curves of COVID-19 cases across methods covid forecasting local
