Statistical approaches for farm and parasitic risk profiling in geographical veterinary epidemiology
Statistical approaches for farm and parasitic risk profiling in geographical veterinary epidemiology
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DOI：
10.1177/0962280212446329
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发表时间：
2012-10-01
影响因子：
2.3
通讯作者：
Biggeri, Annibale
中科院分区：
文献类型：
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作者：
Catelan, Dolores;Rinaldi, Laura;Biggeri, Annibale
我们在兽医流行病学的背景下解决农场和寄生虫风险概况的问题。我们利用在坎帕尼亚地区进行的横断面研究，研究了121个养羊场16种寄生虫的空间分布。我们提出了一个三级分层贝叶斯模型，该模型考虑了多变量空间结构的过度离散性，以获得后验分类概率的估计，即每个寄生虫的后验分类概率，并将概率归入零假设集。我们探索了基于后验概率或后验均值的四种决策规则，并根据错误发现/未发现的数量或错误发现/未发现的比率来比较结果。事实证明，我们的方法对于寄生虫学风险分析是有用的，并且我们证明了决策规则可以很容易地处理。
We address the problem of farm and parasitic risk profiling in the context of Veterinary Epidemiology. We take advantage of a cross-sectional study carried out in the Campania Region in order to study the spatial distribution of 16 parasites in 121 ovine farms. We propose a tri-level hierarchical Bayesian model, which account for multivariate spatially structured overdispersion, to obtain estimate of posterior classification probabilities, that is for each parasite and farm the probability to belong to the set of the null hypothesis. We explore four decision rules based on either posterior probabilities or posterior means and compare the results in terms of the number of false discoveries/non-discoveries or the rate of false discovery/non-discovery. Our approach proved useful for parasitological risk profiling and we show that decision rules can be easily handled.