随着贝丁顿-德安吉利斯发病率函数,日食阶段和奥恩斯坦-乌伦贝克过程的随机病毒感染模型的动态行为
Yuncong Liu1, Yan Wang1, Daqing Jiang1
1College of Science, China University of Petroleum (East China), Qingdao, Shandong 266580, China.
Mathematical biosciences
|January 31, 2024
概括
这项研究引入了一个随机病毒感染模型,具有日食阶段和贝丁顿-德安吉利斯函数. 该研究确定了病毒持久性的条件,并确定了消除病毒的关键因素,帮助治疗策略.
科学领域:
- 数学生物学 数学生物学
- 流行病学 流行病学
- 随机过程 随机过程
背景情况:
- 了解病毒动态对于开发有效治疗方法至关重要.
- 现有的模型经常简化复杂的生物过程,如日食阶段.
- 随机性在生物系统的变性中起着重要作用.
研究的目的:
- 开发和分析一种新的随机病毒感染模型.
- 调查日食阶段和贝丁顿-德安吉利斯功能反应的影响.
- 为了确定病毒持久性和消除的条件.
主要方法:
- 随机微分方程 随机微分方程
- 利亚普诺夫函数和紧的集合分析.
- 强大的数字定律和法图的.
- 分析光谱半径的分析
- 数字模拟的数字模拟.
主要成果:
- 随机模型拥有独特的全局解决方案.
- 在临界条件下存在静止分布,表明T细胞和病毒细胞在临界条件下持久.
- 在准平衡周围推导精确的概率密度函数.
- 确定消除病毒的关键条件.
结论:
- 开发的模型提供了对病毒动态的见解,包括持久性和消除.
- 诸如感染率波动等关键参数显著影响病毒载量.
- 这项研究为了解和潜在控制病毒感染提供了一个理论框架.
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