Assessing intrastate cattle shipments from interstate data and expert opinion

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modeling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the United States. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from one year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modeling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by WAIC and DIC and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau shaped behavior at short distances better were preferred. Further, our study showed that expert data may not guarantee enhanced predictions when expert estimate are disparate.

活体动物运输是养殖主体间传播动物疫病的潜在途径，对于传染病传播建模而言具有关键作用。然而包括美国在内的诸多国家均无法获取完整的接触网络数据集。本研究选取2009年全年的州际兽医检验证书（Interstate Certificate of Veterinary Inspections）的10%样本展开分析，聚焦运输接触的距离依赖性，探究不同函数形式对未观测到的州内运输量估算结果的影响。为进一步提升预测性能，本研究纳入了一项针对州内运输占比的专家征询调查结果。我们采用分层贝叶斯模型（hierarchical Bayesian modeling）估算描述空间核函数及专家数据效应的相关参数，共考虑三种空间核函数形式，并设置纳入与不纳入专家数据两种场景，由此得到共计六组模型。最终得到的六组模型通过广泛适用信息准则（WAIC）与偏差信息准则（DIC）进行排序，并借助样本内与样本外验证开展评估。研究结果显示，空间核函数的形式对州内运输量的预测仅存在轻微影响，但更受青睐的核函数形状需具备如下特征：在大距离区间呈现厚尾特性，同时在短距离区间保持平台型分布。此外，本研究还发现，当专家估计结果存在较大分歧时，纳入专家数据未必能够提升预测性能。