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Quantitative viral dynamics: Methods for parameter estimation
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DOI：10.1016/j.virol.2025.110631.png)
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摘要
En 中文
• 病毒动力学依赖于实验数据的数学建模
• 模型拟合揭示生物学观察现象背后的机制和速率
• 病毒动力学建模应用药代动力学中的严谨统计方法
• 我们综述了个体、群体非线性混合效应和特征拟合
• 我们还分享了用于模型拟合的计算软件包
Keyword:
Viral dynamics
Mathematical model fitting
Parameter estimation
Ordinary differential equations
Residual sum of squares
Maximum likelihood
Nonlinear mixed effects
Markov Chain Monte Carlo
Stochastic approximation expectation maximization
Approximate Bayesian computation
Data science software
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期刊
IF:
2.4
论文数:
528
被引数:
2.1W
机构
引用论文
Mathematical Modeling of Within-Host, Untreated, Cytomegalovirus Infection Dynamics after Allogeneic Transplantation.png)
VIRUSES-BASELOAAI
VIRUSES-BASELIF3.5
OAAI分享收藏Clairon, Q., Pasin, C., Balelli, I., Thiebaut, R., & Prague, M. (2024). Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach. Computational Statistics, 39(6), 2975-3005. https://doi.org/10.1007/s00180-023-01420-xClairon, Q., Pasin, C., Balelli, I., Thiebaut, R., & Prague, M. (2024). 基于常微分方程的非线性混合效应模型参数估计：一种最优控制方法。计算统计学，39(6)，2975-3005. https://doi.org/10.1007/s00180-023-01420-x
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