Center for Occupational and Environmental Health, School of Public Health, University of California, Berkeley, CA, USA.
Adv Exp Med Biol. 2010;673:99-111. doi: 10.1007/978-1-4419-6064-1_7.
The use of mathematical models for developing management options for controlling infectious diseases at alocal scale requires that the structure and parameters of the model reflect the realities of transmission at that scale. Data available to inform local models are generally sparse and come from diverse sources and in diverse formats. These characteristics of the data and the complex structure of transmission models, result in many different parameter sets which mimic the local behavior of the system to within the resolution of field data, even for a model of fixed structure. A Bayesian approach is described, at both a practical and a theoretical level, which involves the assignment of prior parameter distributions and the definition of a semi-quantitative goodness of fit criteria which are essentially priors on the observable outputs. Monte Carlo simulations are used to generate samples from the posterior parameter space. This space is generally much more constrained than the prior space, but with a highly complex multivariate structure induced by the mathematical model. In applying the approach to a model of schistosomiasis transmission in a village in southwestern China, calibration of the model was found to be sensitive to the effective reproductive number, R(eff). This finding has implications both for computation time for the Monte Carlo analysis and for the specification of field data to efficiently calibrate the model for transmission control.
在局部尺度上使用数学模型来制定传染病控制管理方案，要求模型的结构和参数反映该尺度的传播实际情况。用于为局部模型提供信息的数据通常比较稀疏，来自不同的来源和不同的格式。数据的这些特征以及传播模型的复杂结构，导致了许多不同的参数集，这些参数集可以在现场数据的分辨率内模拟系统的局部行为，即使对于固定结构的模型也是如此。本文从实用和理论两个层面描述了一种贝叶斯方法，该方法涉及先验参数分布的分配以及半定量拟合优度标准的定义，这些标准本质上是可观察输出的先验。通过蒙特卡罗模拟从后验参数空间中生成样本。该空间通常比先验空间受约束得多，但由于数学模型具有高度复杂的多元结构，因此受到很大限制。在将该方法应用于中国西南部一个村庄的血吸虫病传播模型时，发现模型的校准对有效繁殖数 R(eff)非常敏感。这一发现不仅对蒙特卡罗分析的计算时间有影响，而且对现场数据的规范也有影响，这对于为传播控制有效地校准模型非常重要。
