World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug–protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19.

中文翻译：

---

大流行性冠状病毒病（COVID-19）的流行病学挑战：人工智能的作用

世界目前正在经历一场由严重急性呼吸综合征冠状病毒分支 2 引起的冠状病毒病 (COVID-19) 大流行造成的重大健康灾难。科学界面临的首要挑战是探索该病毒的生长和传播能力。使用深度学习等人工智能 (AI)，(i) 通过 X 射线或计算机断层扫描 (CT) 或高分辨率 CT (HRCT) 图像快速检测疾病，(ii) 准确预测流行病模式和它们在全球范围内的饱和度，（iii）通过社交网络数据预测疾病和对人群的心理影响，以及（iv）预测药物与蛋白质的相互作用以重新利用药物，引起了广泛的关注。在本研究中，我们描述了各种基于人工智能的技术在 CT 图像中快速有效检测的作用，以补充定量实时聚合酶链反应和免疫诊断分析。还讨论了基于人工智能的技术来预测当前的流行病模式、防止疾病传播和口罩检测。我们根据不同因素检查病毒的传播方式。我们研究深度学习技术来评估最有可能治疗 COVID-19 的药物的亲和力。