Xifeng Yan, Professor of Computer Science, UC Santa Barbara
Achieving State-of-the-Art Performance in COVID-19 Hospitalization Forecasting
Abstract: Demography, population density, business structure, and social culture differ across regions. Correlating these local factors with the number of coronavirus cases could provide more accurate hospitalization forecasts for local policy makers. Different from the classic epidemic models, we developed a data driven forecasting model solely based on deep learning. The model only requires historical data of confirmed cases, hospitalizations and deaths, demographic data of the states and optionally social distancing index. It is able to make predictions of hospitalizations and deaths in a time range of 1-4 weeks without any explicit assumption on the spreading model of Covid19. Our model achieved state-of-the-art performance, outperforming leading algorithms featured at CDC.
Biography: Dr. Yan is a Professor at the University of California at Santa Barbara. He holds the Venkatesh Narayanamurti Chair of Computer Science. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2006. He was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008. He has been working on modeling, managing, and mining graphs in information networks, computer systems, social media and bioinformatics. His works were extensively referenced, with over 9,000 citations per Google Scholar and thousands of software downloads. He received NSF CAREER Award, IBM Invention Achievement Award, ACM-SIGMOD Dissertation Runner-Up Award, and IEEE ICDM 10-year Highest Impact Paper Award.