现象学增长模型的结构和实践可识别性,用于流行病预测
Yuganthi R Liyanage1, Gerardo Chowell2,3, Gleb Pogudin4
1Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, Florida, USA.
ArXiv
|April 1, 2025
概括
这项研究证实,六种常见的流行病学增长模型在结构上是可识别的,并且在疾病预测方面实际上是可靠的. 研究结果支持在现实世界公共卫生场景中使用它们.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 计算生物学 计算生物学
背景情况:
- 当机制未知时,现象学模型对于疾病预测至关重要.
- 模型可靠性取决于参数的结构和实际识别能力.
- 现有的模型面临着非整数指数的挑战.
研究的目的:
- 系统地分析六种常见的流行病学增长模型的可识别性.
- 为严格的可识别性分析重新制定模型.
- 用现实世界流行病学数据验证发现.
主要方法:
- 通过添加状态变量,改革了六种增长模型 (GGM,GLM,Richards,GRM,Gompertz,修改SEIR).
- 通过使用 StructuralIdentifiability.jl (JULIA) 进行结构识别分析.
- 通过使用GrowthPredict (MATLAB) 对,COVID-19和埃博拉数据进行参数估计和预测来验证结果.
- 使用蒙特卡洛模拟在不同噪音水平下评估实际识别能力.
主要成果:
- 所有六个模型在重新编制后都被证实是结构上可识别的.
- 参数估计表明在噪声水平的实际识别和稳定性.
- 在参数估计中观察到模型和数据集的特定灵敏度.
结论:
- 现象生长模型是适应性和可靠的工具,用于预测疾病动态.
- 可识别性分析对于确保这些模型的有效性至关重要.
- 该研究提供了对公共卫生干预模式选择和应用的见解.
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