New model diagnostics for spatio-temporal systems in epidemiology and ecology
New model diagnostics for spatio-temporal systems in epidemiology and ecology
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DOI：
10.1098/rsif.2013.1093
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发表时间：
2014-04-06
影响因子：
3.9
通讯作者：
Gibson, Gavin J.
中科院分区：
文献类型：
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作者：
Lau, Max S. Y.;Marion, Glenn;Gibson, Gavin J.
流行病学和生态学建模的主要挑战是开发有效和易于部署的模型评估工具。这些方法的可用性将大大提高对疾病和生态系统的了解、预测和管理。传统的贝叶斯模型评估工具如贝叶斯因子和偏差信息准则（DIC）是自然的候选工具，但由于其敏感性和复杂性而受到重要的限制。后验预测检查使用从竞争模型中模拟的观察过程的汇总统计数据，可以提供模型拟合的度量，但很难确定适当的统计数据。在这里，我们开发了一种新的方法，通过在贝叶斯分析中嵌入经典思想来诊断一般时空传播模型的错误规范。具体而言，通过提出适当设计的非中心参数化方案，我们构建了潜在残差，其采样特性已知给定模型规格，可用于测量整体拟合，并得出模型中包含的空间和时间过程的错误规格性质的证据。这种模型评估方法可以很容易地作为从后验分布中抽样的标准估计算法的补充来实现，例如马尔科夫链蒙特卡罗。提出的方法首先使用模拟数据进行测试，随后将其应用于描述大猪草在30年内在英国传播的数据。将所提出的方法与后验预测检查和DIC等替代技术进行了比较。结果表明，所提出的诊断工具在评估相互竞争的随机时空传输模型方面是有效的，并且可以提高检测模型错误规格的能力。此外，这里介绍的潜在残余框架很容易扩展到广泛的生态和流行病学模型。
A cardinal challenge in epidemiological and ecological modelling is to develop effective and easily deployed tools for model assessment. The availability of such methods would greatly improve understanding, prediction and management of disease and ecosystems. Conventional Bayesian model assessment tools such as Bayes factors and the deviance information criterion (DIC) are natural candidates but suffer from important limitations because of their sensitivity and complexity. Posterior predictive checks, which use summary statistics of the observed process simulated from competing models, can provide a measure of model fit but appropriate statistics can be difficult to identify. Here, we develop a novel approach for diagnosing mis-specifications of a general spatio-temporal transmission model by embedding classical ideas within a Bayesian analysis. Specifically, by proposing suitably designed non-centred parametrization schemes, we construct latent residuals whose sampling properties are known given the model specification and which can be used to measure overall fit and to elicit evidence of the nature of mis-specifications of spatial and temporal processes included in the model. This model assessment approach can readily be implemented as an addendum to standard estimation algorithms for sampling from the posterior distributions, for example Markov chain Monte Carlo. The proposed methodology is first tested using simulated data and subsequently applied to data describing the spread of Heracleum mantegazzianum (giant hogweed) across Great Britain over a 30-year period. The proposed methods are compared with alternative techniques including posterior predictive checking and the DIC. Results show that the proposed diagnostic tools are effective in assessing competing stochastic spatio-temporal transmission models and may offer improvements in power to detect model mis-specifications. Moreover, the latent-residual framework introduced here extends readily to a broad range of ecological and epidemiological models.