A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
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结合病媒和宿主的多种丰度指数的多元地统计框架：巴西低收入城市社区鼠害案例研究
DOI：
10.1101/2020.07.31.20165753
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Eyre M
中科院分区：
文献类型：
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作者：
Eyre M
地方性病媒传播或人畜共患疾病研究的一个关键要求是估计病媒或储库宿主丰度的空间变异。对于许多病媒物种，有多种丰度指数，但目前在这些指数之间进行选择或将其结合起来的方法没有充分利用建立其联合空间分布模型可能带来的潜在推论好处。在这里，我们开发了一类多变量广义线性地质统计模型的多个丰度指数。我们说明了这种新的方法与案例研究挪威大鼠在低收入的巴西城市社区，老鼠丰度是人类钩端螺旋体病的一个可能的危险因素。我们将老鼠丰度的三个指数联合收割机结合起来，对空间连续的潜在过程（rattiness）进行预测推断，该过程充当丰度的代表。我们将展示如何探索rattinessandspatially变化的环境因素之间的关联，评估每三个贡献指数的相对重要性，并评估剩余的，无法解释的空间变化的存在，并identifyrattinesshotspots。所提出的方法更普遍地适用于作为一种工具，用于了解媒介或水库宿主丰度在预测人类疾病风险的空间变化中的作用。
A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process,rattiness, that acts as a proxy for abundance. We show how to explore the association betweenrattinessand spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identifyrattinesshotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.