This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

这项调查探讨了深度学习如何对抗 COVID-19 大流行，并为未来的 COVID-19 研究提供方向。我们涵盖自然语言处理、计算机视觉、生命科学和流行病学中的深度学习应用。我们描述了这些应用程序如何随着大数据的可用性而变化，以及学习任务是如何构建的。我们首先评估深度学习的现状，最后总结深度学习在 COVID-19 应用中的主要局限性。这些限制包括可解释性、泛化指标、从有限的标记数据中学习以及数据隐私。自然语言处理应用包括挖掘用于信息检索和问答的 COVID-19 研究，以及错误信息检测和公众情绪分析。计算机视觉应用涵盖医学图像分析、环境智能和基于视觉的机器人技术。在生命科学领域，我们的调查着眼于如何将深度学习应用于精准诊断、蛋白质结构预测和药物再利用。深度学习还被用于流行病学的传播预测。我们的文献综述发现了许多深度学习系统对抗 COVID-19 的例子。我们希望这项调查将有助于加速深度学习在 COVID-19 研究中的应用。