MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
J R Soc Interface. 2012 Mar 7;9(68):456-69. doi: 10.1098/rsif.2011.0379. Epub 2011 Aug 10.
Data collected during outbreaks are essential to better understand infectious disease transmission and design effective control strategies. But analysis of such data is challenging owing to the dependency between observations that is typically observed in an outbreak and to missing data. In this paper, we discuss strategies to tackle some of the ongoing challenges in the analysis of outbreak data. We present a relatively generic statistical model for the estimation of transmission risk factors, and discuss algorithms to estimate its parameters for different levels of missing data. We look at the problem of computational times for relatively large datasets and show how they can be reduced by appropriate use of discretization, sufficient statistics and some simple assumptions on the natural history of the disease. We also discuss approaches to integrate parametric model fitting and tree reconstruction methods in coherent statistical analyses. The methods are tested on both real and simulated datasets of large outbreaks in structured populations.
在暴发期间收集的数据对于更好地了解传染病传播和设计有效的控制策略至关重要。但是，由于暴发期间观察到的观察结果之间存在依赖性以及数据缺失，因此对这些数据进行分析具有挑战性。在本文中，我们讨论了应对暴发数据分析中一些持续存在的挑战的策略。我们提出了一种相对通用的统计模型，用于估计传播风险因素，并讨论了针对不同程度数据缺失的估计其参数的算法。我们研究了相对大型数据集的计算时间问题，并展示了如何通过适当使用离散化，充分统计量以及对疾病自然史的某些简单假设来减少计算时间。我们还讨论了将参数模型拟合和树重建方法集成到一致的统计分析中的方法。该方法在结构化人群中的大型暴发的真实和模拟数据集上进行了测试。
