2020/12/08 by Nikos Kargas, Kargas, Nikos, Cheng Qian +9 · 1 citation
Mathematics · #Tensor decomposition and applications
paper · doi:10.48550/arxiv.2012.04747
instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction.