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Spatial measurement error in infectious disease models

机译:传染病模型中的空间测量误差

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Individual-level models (ILMs) for infectious disease can be used to model disease spread between individuals while taking into account important covariates. One important covariate in determining the risk of infection transfer can be spatial location. At the same time, measurement error is a concern in many areas of statistical analysis, and infectious disease modelling is no exception. In this paper, we are concerned with the issue of measurement error in the recorded location of individuals when using a simple spatial ILM to model the spread of disease within a population. An ILM that incorporates spatial location random effects is introduced within a hierarchical Bayesian framework. This model is tested upon both simulated data and data from the UK 2001 foot-and-mouth disease epidemic. The ability of the model to successfully identify both the spatial infection kernel and the basic reproduction number (R_0) of the disease is tested.
机译:在考虑重要的协变量的同时,可以使用传染病的个体级模型(ILM)对个体之间的疾病传播进行建模。确定感染转移风险的一个重要协变量可以是空间位置。同时,在统计分析的许多领域中都存在测量误差,传染病建模也不例外。在本文中,当使用简单的空间ILM建模疾病在人群中的传播时,我们关注的是个人记录位置的测量误差问题。在分层贝叶斯框架内引入了结合了空间位置随机效应的ILM。该模型在模拟数据和英国2001年口蹄疫流行数据上均经过测试。测试了模型成功识别疾病的空间感染核心和基本繁殖数(R_0)的能力。

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