Rauf Hafiz Tayyab, Lali M Ikram Ullah, Khan Muhammad Attique, Kadry Seifedine, Alolaiyan Hanan, Razaq Abdul, Irfan Rizwana
Department of Computer Science, University of Gujrat, Gujrat, Pakistan.
Department of Computer Science, University of Education, Lahore, 54770 Pakistan.
Pers Ubiquitous Comput. 2023;27(3):733-750. doi: 10.1007/s00779-020-01494-0. Epub 2021 Jan 10.
The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.
新型人类冠状病毒疾病COVID-19已成为自1918年流感大流行以来有记录的第五次大流行。COVID-19首次在中国武汉报告，随后在全球范围内传播。世界上几乎所有国家都面临着这一自然挑战。我们提出了预测模型，以估计和预测亚太国家，特别是巴基斯坦、阿富汗、印度和孟加拉国的COVID-19疫情爆发情况。我们利用了最新的深度学习技术，如长短期记忆网络（LSTM）、递归神经网络（RNN）和门控循环单元（GRU），来量化近期大流行的强度。在使用神经网络时，我们考虑了时间变量和数据的非线性。对每个模型的显著特征进行了评估，以预测未来10天内的COVID-19病例数。截至2020年7月1日，所采用的深度学习模型的预测性能准确率超过90%，这表明了本研究所提方法的可靠性。我们希望目前的比较分析能为政府官员提供一幅疫情传播的准确图景，以便他们能够采取适当的缓解措施。
