探索学习方法和疫苗接种的循环动态模型的潜力:奥地利,巴西和中国的COVID-19的比较案例研究
Seyed Ali Rakhshan1, Marzie Zaj1, Fatemeh Helen Ghane1
1Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran.
Physical review. E
|February 17, 2024
概括
本研究介绍了一种混合模型,将动态和学习方法结合起来,用于管理传染病. 动态模型在长期流行病分析方面表现出色,而学习方法在短期预测方面更好.
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:
- 有效的传染病管理需要了解疾病动态和人类行为.
- 疫苗接种,社会干预和遵守卫生协议等因素影响流行病控制.
研究的目的:
- 开发和评估一种创新的混合模型,即组合动态学习模型,将经典动态模型与机器学习技术集成在一起.
- 将传统动态模型的性能与流行病分析和预测的现代学习方法进行比较.
主要方法:
- 开发了一个反复的隔间动态模型 (易感,暴露,接种疫苗,感染,恢复) 来分析流行病的传播和干预影响.
- 采用了四种学习方法:回归树,双向长期短期记忆,封闭反复单元和综合深度学习方法.
- 将模型应用于来自奥地利,巴西和中国的COVID-19数据 (2021年2月至8月),并使用RMSE和R平方指标进行评估.
主要成果:
- 动态模型对于长期的流行病分析,数据匹配和参数识别是有效的.
- 监督学习方法在长期流行病预测方面优于动态模型.
- 动态模型更适合分析流行病行为,而学习方法更适合预测疾病传播.
结论:
- 组合动态学习模型为传染病管理提供了灵活的框架.
- 利用动态建模和机器学习的优势的混合方法对于全面的流行病控制和预测至关重要.
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