Ahuja Sahil, Shelke Nitin Arvind, Singh Pawan Kumar
Thapar University, Patiala, India.
Signal Image Video Process. 2022;16(3):579-586. doi: 10.1007/s11760-021-01988-1. Epub 2021 Jul 23.
The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days' new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.
新型冠状病毒感染(COVID-19)于2019年12月首次在中国武汉出现。被世界卫生组织宣布为全球大流行的COVID-19是全球传播速度最快的疾病。印度是世界上人口第二多的国家,当冠状病毒发展到社区传播呈指数级增长的阶段时,印度仍在与之抗争。因此,研究COVID-19在印度的未来趋势,并预测其在短期内将如何影响经济和社会增长至关重要。在本文中,开发了一种使用卷积神经网络(CNN)和堆叠双向门控循环单元(Bi-GRU)的新型深度学习框架,用于预测和分析印度的COVID-19病例。所提出的模型能够高精度地预测未来30天的新增确诊病例、新增死亡病例、康复率以及控制与健康指数值。将所提出的方法与COVID-19数据集上的高斯过程回归(GPR)模型进行了比较。实验结果表明,与GPR模型相比,所提出的框架在COVID-19预测方面具有高度可靠性。