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 between rattiness and 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 identify rattiness hotspots. 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.

中文翻译：

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一个多变量地质统计框架，用于结合疾病媒介和宿主的多个丰度指数：巴西低收入城市社区的陈腐案例研究

地方性病媒传播或人畜共患病研究的一个关键要求是估计病媒或宿主宿主丰度的空间变化。对于许多载体物种，可以使用多个丰度指数，但目前在这些指数之间进行选择或组合的方法并没有充分利用可能从它们的联合空间分布建模中产生的潜在推论效益。在这里，我们为多个丰度指数开发了一类多元广义线性地质统计模型。我们通过巴西低收入城市社区中挪威大鼠的案例研究来说明这种新颖的方法，其中大鼠的数量可能是人类钩端螺旋体病的风险因素。我们结合了大鼠丰度的三个指数，对空间连续的潜在过程、ratiness、它充当了丰富的代理。我们展示了如何探索老鼠和空间变化环境因素之间的关联，评估三个贡献指数中每一个的相对重要性，并评估剩余的、无法解释的空间变化的存在，并确定老鼠的热点。所提出的方法更广泛地适用于理解载体或储库宿主丰度在预测人类疾病风险空间变化中的作用的工具。