Disease Systems, SAC, West Mains Road, Edinburgh EH9 3JG, UK.
BMC Vet Res. 2012 Sep 10;8:159. doi: 10.1186/1746-6148-8-159.
A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data.
A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates.
The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.
应用流行病学模型的一种常见方法是使用文献中可用的信息确定单个（点估计）参数化。然而，在许多情况下，参数值存在相当大的不确定性，这既反映了当前知识的不完全性，也反映了自然变异，例如在农场之间。此外，模型结果可能对不同的参数值高度敏感。副结核病是一种感染，其许多关键参数值理解不充分且高度可变，对于这种感染，需要开发和应用最大限度利用可用数据的统计技术。
开发了一种基于拉丁超立方抽样的技术，并结合了一种新的重新加权方法，该方法可将参数不确定性和可变性纳入基于模型的流行率估计框架中。该方法通过将其应用于副结核病在奶牛群中的模拟进行了评估，该模拟结合了连续时间随机算法和模型特征，例如疾病发展和脱落的畜群内变异性，这些特征以前在副结核病模型中没有被探索过。生成的样本参数组合被分配一个权重，该权重由量化模型再现流行率数据的能力来确定。一旦生成这些权重，就可以使用模型来评估其他场景，例如控制选项。为了说明这种方法的实用性，使用这些重新加权的模型输出来比较标准测试和淘汰控制策略，这些策略单独使用，以及与旨在降低感染率的简单饲养实践相结合使用。
已证明所开发的技术适用于包含现实控制选项的复杂模型。对于参数不太为人所知或存在显著变异性的模型，重新加权方案允许将参数值的估计分布与其他信息源（例如来自流行率分布的信息）结合使用，从而产生隐含处理变异和不确定性的输出。这种方法允许通过允许参数不确定性和组合不同的信息源来从建模方法中进行更稳健的预测，因此预计在应用于大量疾病系统时将非常有用。
