基于深度学习的混合预测模型用于预测COVID-19在世界上人口最多的国家的传播
1Department of Computer Engineering, Faculty of Engineering, Munzur University, 62100 Tunceli, Turkey.
Expert systems with applications
|June 19, 2023
概括
这项研究引入了一种新的混合深度学习模型,以准确预测COVID-19在人口密的国家的跨国传播. 该模型的性能优于现有方法,有助于健康管理和政策决策.
科学领域:
- 流行病学 流行病学
- 计算健康科学 计算健康科学
- 公共卫生 公共卫生
背景情况:
- COVID-19在全球范围内带来了重大的健康,经济和社会学挑战.
- 准确的流行病传播预测对于有效的卫生管理和政策规划至关重要.
- 现有的研究缺乏对全球人口最多的国家跨境COVID-19传播的分析.
研究的目的:
- 开发和验证COVID-19跨国传播的预测模型.
- 解决在人口密国家分析流行病动态方面的差距.
- 支持卫生工作者,预防措施和健康过程优化.
主要方法:
- 开发了一种混合深度学习模型,集成卷积神经网络 (CNN) 和门式循环单元 (GRU).
- 该模型应用于一个以世界上人口最多的国家为重点的案例研究.
- 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R平方 (R2) 评估了性能.
主要成果:
- 与传统的机器学习和个人深度学习模型 (LR,RF,SVM,MLP,CNN,GRU,LSTM) 相比,混合CNN-GRU模型在预测和分析COVID-19跨国传播方面表现出卓越的表现.
- 该模型有效地将空间特征提取 (CNN) 与长期依赖学习 (GRU) 结合起来.
- 实验结果证实了模型的准确性和稳定性.
结论:
- 开发的混合深度学习模型提供了一种新且有效的方法,用于预测人口密国家跨境COVID-19传播.
- 这种预测能力可以在全球卫生监测和资源分配方面发挥重要作用.
- 该研究强调了混合深度学习架构对于复杂的流行病学预测的潜力.
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