Yu-Xiang Wang harnesses the power of AI to forecast COVID-19

The novel coronavirus continues to exact a tragic toll across the world, damage amplified by a failure to coordinate responses to the COVID-19 pandemic between socially and economically diverse communities. The U.S. stands as an unfortunate example of the grave dangers associated with opening too early without clear official guidance on& prophylactic measures such as wearing masks and social distancing, stressing the need for officials to be able to accurately predict the effects of policy measure on the spread of COVID-19 in their communities. Mind & Machine Intelligence faculty member Yu-Xiang Wang (on the left), in collaboration with UCSB Engineering professor Xifeng Yan (on the right), have begun to address this critical need by applying state-of-the-art deep learning techniques to massive amounts of complex data for modeling the spread of the novel coronavirus. The project, termed Interventional COVID-19 Response Forecasting in Local Communities Using Neural Domain Adaption Models, has been awarded Rapid Response Research (RAPID) grant–for nearly $200,000–from the National Science Foundation(NSF).

More information can be found here in the UCSB Current and also here at Global Health News Wire.