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增强病毒动态建模:可靠的初始估计和准稳态近似 (QSSA) 的有效性条件

Jong Hyuk Byun^1,2, Il Hyo Jung^1,2, Shingo Iwami³

¹Department of Mathematics and Institute of Mathematical Sciences, Pusan National University, Busan, Republic of Korea.

|October 23, 2025

概括

这项研究引入了修订后的病毒准稳态近似模型 (QSSA),该模型纠正了病毒动态建模中的一个关键缺陷. 改进的模型确保了生物准确性,并增强了病毒感染动态的预测能力.

科学领域:

病毒学病毒学
数学生物学数学生物学
计算科学计算科学

背景情况:

数学模型对于理解病毒动态至关重要,基本的病毒模型被广泛用于感染动力学.
准稳态近似 (QSSA) 通常用于简化病毒模型和提高计算效率.
现有的病毒QSSA模型包含一个缺陷,他们错误地认为受感染的细胞和病毒在相同的时间尺度上演变,导致不准确的简化.

研究的目的:

开发一个修订后的QSSA病毒模型,准确考虑受感染细胞和病毒之间的时间尺度分离.
解决和纠正在现有QSSA模型中的模型缩小过程中错误丢失的感染细胞初始值.
建立QSSA在病毒动态中的准确应用的有效性条件.

主要方法:

开发了一个修订后的QSSA病毒模型,包括正确的时间尺度分离.
介绍了一种用于估计受感染细胞初始状况的数学方法.
定义了一个QSSA准确性的有效性条件 (C v = δ / c),需要C v ≪ 1.

主要成果:

修订后的QSSA模型防止了受感染细胞初始值的损失,保持了生物忠实性.
对比分析表明,与现有模型相比,预测准确度和计算效率有所提高.
灵敏度分析证实了修订后的模型的稳定性和关键动态响应的保存.
参数估计显示,在强的时间尺度分离下,真实参数值的恢复更准确.

结论:

修订后的QSSA病毒模型解决了现有模型中的一个关键缺陷,使病毒动态模型更加准确和一致.
纠正受感染的细胞损失问题为病毒学应用提供了强大的框架,特别是有限的实验数据.
这些发现支持使用修订后的QSSA进行可靠的病毒动态分析和预测.

关键词:

参数估计的参数估计.准稳定状态近似方法 (QSSA)灵敏度分析是一种灵敏度分析.有效性条件的有效性条件病毒动态病毒动态

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