Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models presents two challenges: (1) the likelihood function of the models is generally not computable, and computationally intensive simulation-based inference methods need to be employed, and (2) the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences that are driven more by prior assumptions than by the data themselves. We consider a popular and relatively simple yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, also presenting some methodological advances regarding the inference. Using likelihood approximations, we show that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size, which generally has been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision.

译文

:了解传染病的传播动态对于生物学研究和公共卫生应用都很重要。业已广泛证明,统计模型为从发病率和分子数据中推断相关的流行病学数量提供了坚实的基础。但是,传输动态模型的复杂性提出了两个挑战:(1)模型的似然函数通常不可计算,并且需要采用基于计算密集型仿真的推理方法,(2)模型可能无法完全识别从可用数据中。虽然第一个困难可以通过计算和算法上的进步来解决,但第二个障碍则更为根本。可识别性问题可能导致推论更多地是由先前的假设所驱动,而不是由数据本身所驱动。我们考虑了基于经典IS6110指纹数据的流行且相对简单但在分析上难以解决的结核病传播模型。我们报告了模型的可识别性,还介绍了有关推理的一些方法学进展。使用似然近似,我们表明不能从可用数据中识别出生殖价值,并且在先前工作中获得的后验分布很可能主要由假定的先前分布所支配。此外,我们表明,推论受假定的传染人群数量的影响,该数量在以前的工作中通常保持不变。我们证明,如果已经以足够的精确度知道了其余的流行病学参数,就可以推断出感染人群的大小。

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