利用靶细胞限制模型从单次早期测量预测病毒演化
Predicting Viral Evolution from a Single Early Measurement Using the Target Cell Limited Model
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中文总结 AI 辅助
本研究利用靶细胞限制模型，通过坐标变换将非线性病毒动力学转化为单调系统，从单次早期病毒载量测量推导出病毒峰值上界和传染性发作时间下界的解析公式。
中文摘要 AI 辅助
近期诊断技术的进步使得能够准确量化早期病毒载量。一个关键的研究问题是将这些测量转化为具有预测性的临床见解，例如预测患者传染性发作时间和感染严重程度峰值。在本工作中，我们使用靶细胞限制（TCL）模型来解决该问题。由于宿主的内部生物学状态实际上不可观测，从单次含噪声的病毒载量测量预测病毒轨迹极具挑战性。为克服此困难，我们引入了一种新颖的坐标变换，将非线性病毒动力学转化为单调系统。通过利用单调系统理论并考虑变换后系统的不变子空间，我们推导出显式解析公式，该公式基于单次早期病毒观测，建立了病毒载量峰值的严格上界和传染性发作时间的保证下界。
英文摘要
Recent advances in diagnostic techniques have enabled the accurate quantification of early-stage viral loads. A key problem of interest is translating these measurements into predictive clinical insights, such as forecasting a patient's onset of infectiousness and peak infection severity. In this work, we address this problem using the Target Cell Limited (TCL) model. Because the host's internal biological states are practically unobservable, predicting the viral trajectory from a single noisy viral load measurement is highly nontrivial. To overcome this, we introduce a novel coordinate transformation that converts the nonlinear viral dynamics into a monotone system. By leveraging monotone systems theory and taking into account invariant subspaces of the transformed system, we derive explicit analytical formulae that establish a strict upper bound on the peak viral load and a guaranteed lower bound on the time to infectiousness using a single early viral observation.
发表机构
- University of California at Los Angeles（加州大学洛杉矶分校）
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