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利用动态信息的神经网络进行COVID-19传播的预测建模:混合SEIRV-DNNs方法
Cheng Cheng1, Elayaraja Aruchunan2, Muhamad Hifzhudin Noor Aziz3
1Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Scientific reports
|January 15, 2025
概括
一个新的混合模型将神经网络与SEIRV模型结合起来,以准确预测传染病动态. 这种方法增强了理解,并有助于优化针对COVID-19等疾病的公共卫生干预措施.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 了解传染病的时间动态对于公共卫生至关重要.
- 现有的模型可能需要改进,以准确捕捉复杂的疾病传播模式.
研究的目的:
- 开发和验证一种新的混合模型,将深度神经网络与SEIRV模型集成在一起.
- 准确预测传染病的时间演变和未来状态.
主要方法:
- 开发了基于动态信息的神经网络 (DINNs),结合了易感-暴露-感染-恢复-接种疫苗 (SEIRV) 模型.
- 集成微分方程与深度神经网络来预测时间变化的参数.
- 利用来自中国 (1月1日至2022年12月1日) 的报告数据进行实验验证.
主要成果:
- 拟议的DINNs方法准确地学习了疾病动态,并预测了未来的状态.
- 实验结果证明了该模型在真实世界传染病数据上的有效性.
- 混合SEIRV-DNNs模型显示了准确预测疾病轨迹的潜力.
结论:
- 混合SEIRV-DNNs模型为增强传染病预测提供了一个强大的工具.
- 这种方法可以适应其他传染病,如流感和登革热.
- 该模型有助于改善公共卫生战略的预测建模和优化.
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