数学物理学报 ›› 2025, Vol. 45 ›› Issue (1): 269-278.

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具有复发和年龄结构的肺结核病传播模型的参数辨识性研究

武子艺1,2, 杨俊元1,2,*   

  1. 1山西大学复杂系统研究所 太原 030006;
    2山西省疾病防控的数学技术与大数据分析重点实验室 太原 030006
  • 收稿日期:2024-01-16 修回日期:2024-04-09 出版日期:2025-02-26 发布日期:2025-01-08
  • 通讯作者: *杨俊元,E-mail:yjyang66@sxu.edu.cn
  • 基金资助:
    教育部人文社科一般项目 (22YJAZH129)、国家自然科学基金 (12271143, 61573016, 12001339)、山西省回国留学人员科研教研资助项目 (2023-024) 和山西省自然科学基金 (20210302123454)

Study on Parameter Identifiability of an Age-Structured Tuberculosis Model with Relapse

Wu Ziyi1,2, Yang Junyuan1,2   

  1. 1Complex Systems Research Center, Shanxi University, Taiyuan 030006;
    2Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Taiyuan 030006
  • Received:2024-01-16 Revised:2024-04-09 Online:2025-02-26 Published:2025-01-08
  • Supported by:
    Humanities and Social Foundation of Ministry of Education (22YJAZH129), the National Natural Science Foundation of China (12271143, 61573016, 12001339), the Shanxi Scholarship Council of China (2023-024) and the Shanxi Province Science Foundation (20210302123454)

摘要: 模型的参数辨识性是判断模型预测准确与否的关键. 依赖可辨识性结果的模型预测更为科学和准确. 相较于常微分方程模型, 具有初边值条件的年龄结构传染病模型参数辨识问题存在较大挑战. 该文利用公共卫生科学数据中心报告数据探讨具有年龄结构和复发的肺结核病模型的参数辨识问题. 首先利用特征值法得到模型参数结构辨识可能性的先后顺序, 其次通过蒙特卡洛实验计算各参数的平均相对误差发现模型参数是实用可辨识的. 进一步, 通过计算 Fisher 信息矩阵及偏秩相关性分析讨论模型中参数的不确定性对肺结核病传播的影响.

关键词: 肺结核, 辨识性分析, 敏感性分析

Abstract: The identifiability of model parameters plays a crucial role in determining the precision of model predictions. Additionally, predictions based on identifiable outcomes exhibit a higher degree of scientific rigor and accuracy. Unlike ordinary differential systems, achieving parameter identifiability in age-structured models with initial-boundary conditions poses considerable challenges. This paper aims to investigate the structural and practical identifiability of an age-structured tuberculosis model with relapse. First, we employ the eigenvalue method to ascertain the order of unidentifiable parameters. In conjunction with data provided by the Public Health Science Data Center, we employ Monte Carlo simulation to explore the practical identifiability of the proposed model. By calculating the Average Relative Error (ARE) for each parameter and utilizing the Fisher information matrix, we determine that all parameters are identifiable. Furthermore, we assess how uncertainty in these parameters affects tuberculosis transmission by analyzing the Fisher information matrix and partial rank correlation coefficient.

Key words: tuberculosis, identifiable analysis, sensitivity analysis

中图分类号: 

  • O29