延迟分数顺序的建模和分析COVID-19 SEIHRM模型与马来西亚的媒体报道
Rui Hu1, Muhamad Hifzhudin Noor Aziz1, Elayaraja Aruchunan2
1Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Scientific reports
|July 13, 2025
概括
这项研究引入了一种新的分数顺序SEIHR-M模型,用于分析马来西亚COVID-19传播的时间延迟. 媒体的影响和记忆效应显著影响流行病的动态,较长的延迟促进周期性振荡.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 分数微积分的计算.
背景情况:
- COVID-19的传播动态很复杂,受到生物因素和人类行为的影响.
- 现有的模型可能无法完全捕捉到记忆效应和延迟的行为反应,这对于了解疾病传播至关重要.
- 媒体的影响是流行病建模中的一个重要,但往往被简化的因素.
研究的目的:
- 开发和分析马来西亚COVID-19的延迟分数顺序SEIHR-M模型.
- 调查分数动态和时间延迟媒体对传输的影响.
- 评估媒体干预在控制疫情中的作用.
主要方法:
- 开发一个分数顺序的SEIHR-M流行病模型,时间延迟.
- 使用下一代矩阵对模型解决方案,平衡和稳定性的理论分析.
- 基本复制号 (R0) 的导数.
- 数字模拟和模型与马来西亚早期的COVID-19数据相匹配.
- 对模型参数的灵敏度分析,特别是媒体影响.
主要成果:
- 模型解决方案是生物可行的 (非负和有界).
- 当R0 < 1时,无病平衡是稳定的,无论延迟时间如何.
- 固有平衡稳定性取决于分数顺序和延迟,Hopf分叉导致振荡.
- 增加的分数顺序和延迟增强了振荡行为,表明反复出现的流行病波.
- 分数顺序延迟模型与整数顺序模型相比,显示出优越的适配性能.
- 媒体干预显著影响流行病的传播,减少感染峰值.
结论:
- 拟议的延迟分数顺序SEIHR-M模型准确地捕捉了马来西亚的COVID-19传播动态.
- 分数动态和时间延迟的媒体影响对于理解流行病的复杂性和振荡至关重要.
- 媒体干预是控制COVID-19的有效工具,强调及时传播信息的重要性.
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