STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization

@article{Kargas2020STELARST,
  title={STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization},
  author={Nikos Kargas and Cheng Qian and Nicholas D. Sidiropoulos and Cao Xiao and Lucas Glass and Jimeng Sun},
  journal={ArXiv},
  year={2020}
}
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods… 

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