Instituto Gulbenkian de Ciência, Oeiras, Portugal.
PLoS One. 2011;6(5):e19616. doi: 10.1371/journal.pone.0019616. Epub 2011 May 24.
Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.
生物学中的数学模型是研究和探索复杂动态的有力工具。然而，要使理论结果与实验观测相一致，就必须承认我们对真实系统的理论表示存在很大的不确定性。正确处理这些不确定性是成功使用模型来预测实验或现场观测的关键。多年来，许多模型校准和参数估计工具已经解决了这个问题。在本文中，我们提出了一个用于不确定性分析和参数估计的通用框架，旨在处理与动态生物系统建模相关的不确定性，同时对所使用的模型类型保持不可知论。我们将该框架应用于拟合类似于 SIR 的流感传播模型，以拟合三个欧洲国家（比利时、荷兰和葡萄牙）的 7 年发病率数据。
