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Accurate prediction of the transmission of epidemic diseases such as COVID-19is crucial for implementing effective mitigation measures. In this work, wedevelop a tensor method to predict the evolution of epidemic trends for manyregions simultaneously. We construct a 3-way spatio-temporal tensor (location,attribute, time) of case counts and propose a nonnegative tensor factorizationwith latent epidemiological model regularization named STELAR. Unlike standardtensor factorization methods which cannot predict slabs ahead, STELAR enableslong-term prediction by incorporating latent temporal regularization through asystem of discrete-time difference equations of a widely adoptedepidemiological model. We use latent instead of location/attribute-levelepidemiological dynamics to capture common epidemic profile sub-types andimprove collaborative learning and prediction. We conduct experiments usingboth county- and state-level COVID-19 data and show that our model can identifyinteresting latent patterns of the epidemic. Finally, we evaluate thepredictive ability of our method and show superior performance compared to thebaselines, achieving up to 21% lower root mean square error and 25% lower meanabsolute error for county-level prediction.

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization