将分数顺序SEI1I2I3QCR模型与意识和非药物干预相集成,以实现最佳的COVID-19流行病流行
Ahmed Refaie Ali1, Daniyal Ur Rehman2, Najeeb Alam Khan3
1Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El Kom, Menofia, 32511, Egypt. ahmed.refaie@science.menofia.edu.eg.
BMC medical research methodology
|February 22, 2025
概括
本研究介绍了一种先进的SEI1I2I3QCR模型,使用分数计算和时间延迟来准确预测COVID-19动态. 该模型有效地模拟非药物干预,降低感染率并帮助流行病控制战略.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 传染病建模 传染病建模
背景情况:
- COVID-19 引发了持续的全球健康挑战,需要超越传统方法的先进控制策略.
- 传统的流行病模型可能无法完全捕捉像COVID-19这样的疾病的复杂动态,包括延迟和传播变异.
研究的目的:
- 开发和验证一个先进的SEI1I2I3QCR分区模型,其中包含分数顺序延迟微分方程.
- 分析时间延迟和分数顺序对疾病动态和稳定性的影响.
- 使用最佳控制理论评估非药物干预 (NPI) 的有效性.
主要方法:
- 开发一种新的SEI1I2I3QCR流行病模型,使用分数顺序延迟微分方程.
- 关于延迟和分数顺序参数的模型稳定性分析.
- 应用最佳控制理论来模拟和评估NPI,如隔离和宣传活动.
- 数字模拟和对来自中国的真实世界COVID-19数据的验证.
主要成果:
- 分数顺序延迟微分方程准确地捕捉了COVID-19的进展和峰值振荡.
- 稳定性分析确定了延迟和分数顺序参数在疾病管理中的关键作用.
- 模拟的NPI,包括隔离和宣传活动,显示出感染率的显著降低.
- 模型模拟显示与来自中国的经验 COVID-19 数据有很强的一致性.
结论:
- 整合时间延迟因子和分数计算为流行病建模提供了更准确的框架.
- 开发的模型为指导流行病应对策略提供了有价值的工具.
- 使用这种建模方法,可以有效设计和实施有针对性的,具有成本效益的控制措施.
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