Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; and Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, patient care, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.

正如目前面临的COVID-19疾病一样，全球卫生有时会面临大流行。这种疾病的传播和感染因素很高。自首次报告出现以来的六个月内，来自大多数国家/地区的大量人受到感染，并且还在不断蔓延。所需的系统尚未准备就绪，无法应对大流行；因此，必须减轻现有容量。另一方面，现代在很大程度上取决于包括数据科学在内的人工智能。深度学习（DL）是这些技术的当前旗手之一。它可以在站点传播，疾病诊断，药物和疫苗发现，治疗，患者护理等方面缓解像大流行这样的COVID-19。但是此DL需要大型数据集以及强大的计算资源。普遍的流行病缺乏可靠的数据集。因此，深度迁移学习（DTL）可以从一项任务中学习，并且可以完成另一项任务，因此非常有效。此外，物联网，网络摄像头，无人机，智能医疗设备，机器人等边缘设备（ED）在大流行情况下也非常有用。这些类型的设备使基础架构变得复杂和自动化，有助于应对爆发。但是这些设备的计算资源很低，因此，应用DL还是有点挑战。因此，DTL在那里也将是有效的。本文对这些问题的潜力和挑战进行了学术研究。它描述了相关的技术背景以及对相关最新技术的评论。