System identifiability in a time-evolving agent-based model.
System identifiability in a time-evolving agent-based model.
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DOI：
10.1371/journal.pone.0290821
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发表时间：
2024
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
数学模型是研究和预测传染性病原体传播的重要工具。模型模拟和预测的准确性总是取决于模型参数的规格。因此，对这些参数的估计极为重要;然而，虽然有些参数可以从观察性研究中得出，但其他参数的值却难以测量。相反，模型可以与推理算法（即，数据同化方法或统计滤波器），其将模型模拟拟合到现有观测并估计未观测的模型状态变量和参数。理想情况下，这些推理算法应该为给定的模型和一组观测值找到最佳拟合解;然而，由于这些估计量是未观测到的，因此通常不确定是否已识别出正确的参数。此外，对于基于特定模型形式定义的抽象参数，“正确”的真正含义并不清楚。在这项工作中，我们探讨了随机系统中的不可识别性问题，当被忽视时，可以显着阻碍模型预测。我们使用了一个网络，基于代理的模型来模拟耐甲氧西林金黄色葡萄球菌（MRSA）在医院环境中的传播，并尝试使用Encampaign Adjustment Kalman Filter（一种有效的贝叶斯推理算法）来推断关键模型参数。我们表明，即使推理方法收敛，模拟使用估计的参数产生的协议与观察，真正的参数是不完全可识别的。虽然模型推断系统可以排除不太可能包含真实参数的参数空间的大部分区域，但估计的参数范围仍然包括可以同样很好地拟合观测的多个参数组合。我们表明，分析合成轨迹可以支持或反驳索赔的可识别性。虽然我们在特定的模型系统上执行此操作，但这种方法可以推广到部分可观测系统的各种随机表示。我们还建议旨在提高可识别性的数据操作，这可能适用于许多感兴趣的系统。
Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what ‘correct’ really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.
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影响因子：
5
作者：
Cooper, Ben S.;Medley, Graham F.;Bradley, Susan J.;Scott, Geoffrey M.
通讯作者：
Scott, Geoffrey M.
DOI：
10.1073/pnas.2111190118
发表时间：
2021-09-14
影响因子：
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