Utku Anil
Department of Computer Engineering, Faculty of Engineering, Munzur University, 62100 Tunceli, Turkey.
Expert Syst Appl. 2023 Nov 30;231:120769. doi: 10.1016/j.eswa.2023.120769. Epub 2023 Jun 12.
COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.
新冠病毒病具有疾病和健康现象，并产生社会学和经济方面的不利影响。准确预测疫情传播将有助于卫生管理规划以及经济和社会学行动计划的制定。在文献中，有许多研究分析和预测新冠病毒病在城市和国家的传播情况。然而，尚无研究对世界人口最多的国家之间的跨国传播进行预测和分析。本研究旨在预测新冠病毒病疫情的传播。这项研究的动机是通过预测新冠病毒病疫情的传播来减轻卫生工作者的工作量、采取预防措施并优化卫生流程。开发了一种混合深度学习模型来预测和分析新冠病毒病的跨国传播，并针对世界人口最多的国家进行了案例研究。使用均方根误差（RMSE）、平均绝对误差（MAE）和相关系数（R）对所开发的模型进行了广泛测试。实验结果表明，所开发的模型在预测和分析世界人口最多的国家的新冠病毒病跨国传播方面比逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）、多层感知器（MLP）、卷积神经网络（CNN）、门控循环单元（GRU）、长短期记忆网络（LSTM）和基础卷积神经网络 - 门控循环单元模型更成功。在所开发的模型中，卷积神经网络执行卷积和池化操作以从输入数据中提取空间特征。门控循环单元则对卷积神经网络推断出的长期和非线性关系进行学习。所开发的混合模型比其他对比模型更成功，因为它能够将卷积神经网络和门控循环单元模型的有效特征结合使用。对世界人口最多的国家的新冠病毒病跨国传播进行预测和分析可被视为本研究的一个创新点。
