预测牛结核病动态的牛和农场耦合模型与獾模型（TB）
Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)
摘要：
牛结核病是几个国家农业的主要问题。结核病可以通过牛以外的物种感染和传播，这可能会给疾病控制带来问题。在英国和爱尔兰，獾是公认的感染宿主，并对潜在的控制策略进行了大量讨论。我们提出了一个耦合的个人为基础的模型，牛结核病的獾和牛，其目的是捕捉疾病的自然历史和两个物种在大约县规模的关键细节。该模型在空间上是明确的，它遵循了非常大量的牛和獾在不同的网格大小为每个物种，也包括冬季住房。我们表明，该模型可以复制牛和獾种群的动态报告，以及牛的疾病的患病率不断增加。使用拉丁超立方体采样对模拟中用作输入的参数空间进行扫出，并使用混合效应模型对模型输出进行敏感性分析。通过探索一个大的和计算密集型的参数空间，我们表明，可用的控制策略，它是结核病检测的频率和是否实行冬季住房，感染牛的数量有最显着的影响，冬季住房的影响变得越来越强，农场规模的增加。獾是否被扑杀解释了约5%，而检测感染牛的测试的准确性解释了感染牛数量的方差不到3%。
英文摘要：
Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle.
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