Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models
Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models
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DOI：
10.1186/s12976-018-0097-6
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发表时间：
2019-01-14
影响因子：
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通讯作者：
Chowell, Gerardo
中科院分区：
文献类型：
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作者：
Roosa, Kimberlyn;Chowell, Gerardo
数学建模现在经常用于暴发调查，以了解传染病动力学的潜在机制，评估流行病学数据中的模式，并预测流行病的发展轨迹。然而，成功地应用数学模型来指导公共卫生干预，在于能够可靠地估计模型参数及其相应的不确定性。方法描述了一种从动态系统中生成模拟数据以量化参数不确定性和可辨识性的参数自举方法。我们计算估计参数分布的可信区间和均方误差来评估参数的可识别性。为了演示这一方法，我们从低复杂性的SEIR模型开始，并通过与大流行流感、埃博拉和寨卡病毒的应用相对应的越来越复杂的分区模型的示例来工作。结果总的来说，更复杂的模型(基于方程/状态和参数的数量)更有可能出现参数可识别性问题。随着被联合估计的参数数量的增加，平均而言，围绕估计参数的不确定性也趋于增加。我们发现，在大多数情况下，R-0对于影响模型中单个参数的参数可辨识性问题通常是稳健的。结论由于公共卫生政策会受到数学建模研究结果的影响，在将模型与现有数据进行拟合之前进行参数可辨识性分析，并报告具有量化不确定性的参数估计是很重要的。所描述的方法在这些方面是有帮助的，并增强了使用隔室动态模型进行基于模型的推理的基本工具包。
BackgroundMathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models.MethodsWe describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika.ResultsOverall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R-0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R-0 can still be estimated with precision and accuracy.ConclusionsBecause public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.