Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A Primer for parameter uncertainty, identifiability, and forecasts.
Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A Primer for parameter uncertainty, identifiability, and forecasts.
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DOI：
10.1016/j.idm.2017.08.001
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发表时间：
2017-08
影响因子：
8.8
通讯作者：
Chowell G
中科院分区：
文献类型：
--
作者：
Chowell G
数学模型提供了一个定量框架，科学家可以利用它评估关于潜在潜在机制的假设，这些机制解释了不同时空尺度上观测数据的模式，生成关键动力学参数的估计值，评估干预措施的影响，优化控制策略的影响，并生成预测。我们回顾并说明了一个简单的数据同化框架，用于校准数学模型的基础上，常微分方程模型，使用时间序列数据描述的时间进展的情况下计数，例如，人口增长或传染病传播动力学。与总是提出如何设置参数先验的问题的贝叶斯估计方法相反，这种频率论方法依赖于对数据中的误差结构进行建模。我们讨论了有关参数的可识别性，不确定性量化和传播，以及模型性能和预测沿着例子的基础上，使用模拟和真实的数据集参数化的现象学和机械模型。
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales, generate estimates of key kinetic parameters, assess the impact of interventions, optimize the impact of control strategies, and generate forecasts. We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating, for instance, to population growth or infectious disease transmission dynamics. In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters, this frequentist approach relies on modeling the error structure in the data. We discuss issues related to parameter identifiability, uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.