Motivated by foot-and-mouth disease (FMD) outbreak data from Turkey, we develop a model to estimate disease risk based on a space-time record of outbreaks. The spread of infectious disease in geographical units depends on both transmission between neighbouring units and the intrinsic susceptibility of each unit to an outbreak. Spatially correlated susceptibility may arise from known factors, such as population density, or unknown (or unmeasured) factors such as commuter flows, environmental conditions, or health disparities. Our framework accounts for both space-time transmission and susceptibility. We model the unknown spatially correlated susceptibility as a Gaussian process. We show that the susceptibility surface can be estimated from observed, geo-located time series of infection events and use a projection-based dimension reduction approach which improves computational efficiency. In addition to identifying high risk regions from the Turkey FMD data, we also study how our approach works on the well known England-Wales measles outbreaks data; our latter study results in an estimated susceptibility surface that is strongly correlated with population size, consistent with prior analyses.

翻译：根据土耳其的口蹄疫爆发数据,我们开发了一种模型,根据爆发的时空记录来估计疾病风险。传染病在地理单位的传播取决于相邻单位之间的传播和每个单位对爆发的内在易感性。空间相关易感性可能来自已知因素,如人口密度,或通勤量、环境条件或健康差异等未知(或未测量)因素。我们的框架既包括时空传播,也包括易感性。我们把未知的空间相关易感性作为高斯过程的模型。我们显示,易感表面可以从观测到的、地理分布的感染事件时间序列中估算出来,并使用基于预测的减少维度的方法来提高计算效率。除了从土耳其FMD数据中查明高风险区域外,我们还研究我们的方法如何利用众所周知的英格兰-威尔士麻疹爆发数据;我们的后一项研究的结果是,估计的易感表面与人口规模密切相关,与先前的分析一致。