一个新的COVID-19混合预测模型,每天新病例数据
Guohui Li1, Jin Lu1, Kang Chen1
1School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.
Engineering applications of artificial intelligence
|April 15, 2024
概括
一个新的混合模型通过分解数据,判断复杂性和使用专门的预测方法,准确地预测COVID-19病例. 这种方法显著提高了全球流行病预防工作的预测准确度.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 新的COVID-19菌株如Delta和Omicron的出现导致全球感染率创纪录.
- 准确预测每日新增的COVID-19病例对于有效的流行病学预防策略至关重要.
研究的目的:
- 提出一种新的混合预测模型,以提高COVID-19每日新病例数据预测的准确性.
- 通过提供更可靠的感染数量预测,改善流行病学预防.
主要方法:
- 单元光谱分解 (SSD) 将COVID-19数据分解为单元光谱组件 (SSC).
- 神经网络对时间的估计,以将SSC分为高复杂性和低复杂性组.
- 一个改进的最小平方支向量机 (GLSSVM),由GODLIKE算法优化,用于预测高复杂性的SSC.
- 预测低复杂度SSC的ARIMA模型和预测高复杂度SSC的预测错误的GLSSVM.
主要成果:
- 日本,德国和俄罗斯的模拟实验表明,与现有模型相比,预测准确度更高,错误率更低.
- 迪博德玛丽亚诺 (DM) 测试证实了该模型的全面评估和有效性.
- 在日本,与ARIMA模型相比,拟议的模型实现了93.17%的根平均平方误差 (RMSE),91.42%的平均误差和81.20%的平均绝对百分比误差 (MAPE) 的显著降低.
结论:
- 拟议的混合模型在COVID-19病例预测准确度方面取得了重大进展.
- 该模型能够处理数据的复杂性和纠正错误,使其成为全球流行病监测的宝贵工具.
- 这些发现强调了混合建模方法在打击传染病爆发方面的潜力.
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