Despite efforts throughout the United States last spring to suppress the spread of the novel coronavirus, states across the country have experienced spikes in the past several weeks. The number of confirmed COVID-19 cases in the nation has climbed to more than 3.5 million since the start of the pandemic.
Public officials in many states, including California, have now started to roll back the reopening process to help curb the spread of the virus. Eventually, state and local policymakers will be faced with deciding for a second time when and how to reopen their communities. A pair of researchers in UC Santa Barbara’s College of Engineering, Xifeng Yan and Yu-Xiang Wang, have developed a novel forecasting model, inspired by artificial intelligence (AI) techniques, to provide timely information at a more localized level 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,” said Yan, an associate professor who holds the Venkatesh Narayanamurti Chair in Computer Science. “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. 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 forecasting project, “Interventional COVID-19 Response Forecasting in Local Communities Using Neural Domain Adaption Models,” received a Rapid Response Research (RAPID) grant for nearly $200,000 from the National Science Foundation (NSF).
“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,” said Wang,