使用EMD-ANN混合模型预测COVID-19流行病的方法方法
1Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China.
Internet of things (Amsterdam, Netherlands)
|April 15, 2024
概括
这项研究引入了一种混合模型,将集体实证模式分解 (EEMD) 和人工神经网络 (ANN) 结合起来,以准确预测COVID-19疫情. 这种新的方法优于传统方法,有助于医疗保健管理.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 由新型冠状病毒引起的COVID-19大流行,对全球医疗保健系统提出了重大挑战.
- 准确预测流行病的传播对于有效的资源分配和公共卫生干预至关重要.
- 数据有限和复杂的动态使COVID-19的预测成为一个困难的任务.
研究的目的:
- 开发和评估一种用于预测COVID-19流行病的混合模型.
- 通过整合集体实证模式分解 (EEMD) 与人工神经网络 (ANN) 来提高预测准确性.
- 为医疗保健管理和预防行动提供一个工具.
主要方法:
- 提出了一个混合模型,将EMD和ANN结合起来.
- 利用了2020年1月22日至2020年5月18日的实时COVID-19时间序列数据.
- EEMD用于数据无声化和分解成子信号,随后进行ANN培训.
主要成果:
- 拟议的混合EEMD-ANN模型与传统的统计分析方法相比,表现优越.
- 该模型有效地预测了COVID-19流行趋势,使用了无效的时间序列数据.
- 结果表明该模型在可靠的流行病预测方面的潜力.
结论:
- 混合型EEMD-ANN模型对准确的COVID-19流行病预测有显著的前景.
- 这种预测能力可以帮助政府和医疗保健提供者规划和实施及时干预.
- 该研究强调了先进的计算模型在管理公共卫生危机中的价值.
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