在扩散性流行病模型中,复杂的动态和模式形成具有感染依赖的恢复率
Wael El Khateeb1, Chanaka Kottegoda2, Chunhua Shan1
1Department of Mathematics and Statistics, The University of Toledo, Toledo, OH, 43606, USA.
Mathematical biosciences
|December 20, 2025
概括
这项研究引入了一个扩散性流行病模型,其中恢复取决于感染水平. 更快的易感移动驱动空间模式,突出了针对区域疾病浪潮的有针对性的战略的需要.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 动态系统 动态系统
背景情况:
- 流行病模型对于了解疾病传播至关重要.
- 感染依赖的恢复率和空间动态是影响疾病传播的关键因素.
- 分叉分析是研究数学模型中复杂行为的一个强大工具.
研究的目的:
- 制定和分析一个扩散性流行病模型,感染取决于康复率.
- 研究由扩散驱动的空间和时空模式的出现.
- 了解易受感染和感染人口在模式形成中的作用.
主要方法:
- 对反应动力学进行分叉分析,以确定稳态和周期溶液.
- 分析扩散驱动的不稳定性,敏感个体作为抑制剂,感染个体作为激活剂.
- 对k模式图灵不稳定性和 (k1,k2) 模式图灵-霍夫分叉进行模式形成的研究.
- 检查从时间振荡到空间模式的短暂动态.
主要成果:
- 该模型表现出多个恒定的稳定状态和空间均的周期解.
- 观察到扩散驱动的不稳定性,导致模式形成.
- 易感种群的快速移动诱导空间和空间时间模式.
- 确定了异步疾病复发,空间模式波浪和局部热点.
结论:
- 该模型展示了复杂的传播动态,包括模式形成和局部爆发.
- 针对空间的干预策略对于控制区域差异和周期性疾病浪潮至关重要.
- 了解扩散和反应动力学之间的相互作用对于有效的流行病控制至关重要.
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