一个分数顺序流感流行病模型的分析和最佳控制
Fatima Zahra1, Zhiwu Li2, Abdulrahman Al-Ahmari3,4
1School of Electro-Mechanical Engineering, Xidian University, Xi'an, 710071, China.
Scientific reports
|December 6, 2025
概括
空气污染显著影响流感疫情,因为系统记忆效应延长了流感的持续时间. 有效的控制策略必须考虑到这些环境影响和历史的依赖性,以持续缓解.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 环境健康 环境健康
背景情况:
- 环境空气污染和季节性流感对公众健康构成重大挑战.
- 传统的流行病学模型往往忽略了对疾病传播的累积环境影响.
- 了解污染对流感动态的长期影响至关重要.
研究的目的:
- 开发一种包含系统记忆效应的小数序流行病学模型.
- 分析持续空气污染对流感传播动态的影响.
- 为有效的干预策略应用分数最佳控制理论.
主要方法:
- 开发一个分数顺序的SEIIHRD模型,使用Atangana-Baleanu Caputo (ABC) 导数.
- 通过ABC导数的Mittag-Leffler内核集成系统内存来表示污染的持久影响.
- 导出基本复制号和分析模型动态.
- 分数最佳控制理论的应用用于干预优化.
主要成果:
- 与经典模型相比,较低的分数顺序 (代表更强的系统内存) 显著延长了流感爆发.
- 持续和及时的控制措施是有效减轻疾病的必要条件.
- 最佳干预持续时间对系统内存的强度高度敏感,突出显示了历史依赖的重要性.
结论:
- 分数计算为模拟由环境因素影响的复杂疾病动态提供了一个强大的工具.
- 考虑系统内存效应,例如空气污染的影响,对于准确预测和控制流感疫情至关重要.
- 有效的公共卫生战略需要考虑长期的环境影响和疾病传播的历史依赖.
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