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神经SEIR:一个灵活的数据驱动框架,用于精确预测流行病的疾病

Haoyu Wang¹, Xihe Qiu¹, Jinghan Yang¹

¹School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Mathematical biosciences and engineering : MBE

|November 3, 2023

概括

本研究介绍了Neural-SEIR,这是一个新的框架,将神经网络与传统的流行病学模型相结合,以改善流行病预测. 神经SEIR通过将先前的知识与数据驱动的见解相结合,提高了对COVID-19等复杂疾病的预测准确度.

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:

精确的流行病建模对于疾病控制和死亡率降低至关重要.
由于不可预测的因素,像易受-暴露-感染-恢复 (SEIR) 等传统模型与复杂的流行病 (例如COVID-19) 斗争.
数据驱动的方法缺乏对小数据集的概括性和准确性,没有事先的知识.

研究的目的:

开发一个灵活的,数据驱动的框架,以提高流行病预测的准确性.
克服传统流行病学和纯数据驱动模型的局限性.
引入Neural-SEIR,一种混合模型,将先前知识与先进的机器学习相结合.

主要方法:

开发了Neural-SEIR,这是一个通过将参数与神经网络近似来"神经化"SEIR模型的框架.
使用长短期记忆 (LSTM) 网络用于复杂的相关性和指数式平滑 (ES) 用于季节性.
将SEIR模型参数集成到神经网络结构中,以利用先前的知识.

主要成果:

与传统的机器学习和流行病学模型相比,神经SEIR表现优越.
在预测流行病方面实现了高预测准确性和效率.
有效捕捉疾病传播中的复杂相关性和季节性模式.

结论:

神经SEIR框架为准确的流行病预测提供了一个强大的方法.
将先前知识与数据驱动技术相结合的混合模型显示出显著的前景.
这种方法可以改善对新出现的传染病的公共卫生反应.

关键词:

神经-SEIR 的使用.传染病的预测和预测.长期短期记忆长期短期记忆时间序列数据数据时间序列数据

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