Kargas Nikos, Qian Cheng, Sidiropoulos Nicholas D, Xiao Cao, Glass Lucas M, Sun Jimeng
Dept. of ECE, University of Minnesota.
Analytics Center of Excellence, IQVIA.
ArXiv. 2020 Dec 8:arXiv:2012.04747v2.
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 which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations of a widely adopted epidemiological model. We use 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.
准确预测新冠疫情等传染病的传播对于实施有效的缓解措施至关重要。在这项工作中，我们开发了一种张量方法来同时预测多个地区的疫情趋势演变。我们构建了一个病例数的三维时空张量（地点、属性、时间），并提出了一种带有潜在流行病学模型正则化的非负张量分解方法，称为STELAR。与标准张量分解方法不同，标准方法无法提前预测数据块，而STELAR通过一个广泛采用的流行病学模型的离散时间差分方程组纳入潜在的时间正则化，从而实现长期预测。我们使用 而不是地点/属性层面的流行病学动态来捕捉常见的疫情概况子类型，并改善协同学习和预测。我们使用县级和州级的新冠疫情数据进行实验，结果表明我们的模型能够识别出疫情中有趣的潜在模式。最后，我们评估了我们方法的预测能力，与基线相比显示出卓越的性能，在县级预测中，均方根误差降低了21%，平均绝对误差降低了25%。 （注：原文中“ We use instead of location/attribute-level epidemiological dynamics”处“use”后缺少内容，翻译时保留原文状态）
