Spatial disease mapping using the Poisson-Gamma model

R. Jainsankar, M. Ranjani
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Abstract

In disease mapping, it is preferable to estimate the risk rather than the significance in general, but the variation in estimation precision across the geographical map of the study region must also be taken into consideration. In such a situation the conventional methods would not yield the best estimates. Heterogeneity is an important aspect to be considered as significant in Disease Mapping and relative risk estimation. The simple regression models often do not capture the extent of the variation exhibited in the spatial count data. This is the case when the spatial data is over-dispersed or there is spatial correlation due to unobserved confounders. In such situations, it is appropriate to include some additional terms, which may be in the form of the prior distribution. In this paper, a Poisson model with Gamma prior is used to model and map the dengue incidences in Tamil Nadu to explain the patterns of variations.
使用泊松-伽玛模型的空间疾病制图
在疾病制图中,最好是估计风险,而不是一般意义上的显著性,但也必须考虑到研究区域地理地图上估计精度的差异。在这种情况下,传统方法无法得出最佳估计。异质性在疾病制图和相对风险估计中是一个重要的方面。简单的回归模型往往不能捕捉空间计数数据中显示的变化程度。当空间数据过度分散或由于未观察到的混杂因素而存在空间相关性时,就会出现这种情况。在这种情况下,适当地包括一些附加条款,这些条款可能以先验分布的形式出现。在本文中,一个泊松模型与伽玛先验被用来模拟和绘制登革热发病率在泰米尔纳德邦,以解释变化的模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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