Point process methodology for on-line spatio-temporal disease surveillance
Point process methodology for on-line spatio-temporal disease surveillance
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DOI：
10.1002/env.712
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发表时间：
2005-08-01
期刊：
影响因子：
1.7
通讯作者：
Su, TL
中科院分区：
文献类型：
--
作者：
Diggle, P;Rowlingson, B;Su, TL
我们提出了在线时空疾病监测问题，即预测点过程的空间和时间变化强度超过预先指定的阈值的空间和时间局部偏移，其中每个点代表所讨论疾病的个体病例。我们的点过程模型是一个非平稳对数高斯 Cox 过程，其中时空强度 lambda (x, t) 乘法分解为两个确定性分量，一个描述正常疾病发病率模式中的纯空间变化，另一个描述正常疾病发病模式的纯时间变化，以及一个代表空间和时间局部偏离正常模式的未观察到的随机分量。我们给出了估计模型参数以及对电流强度进行概率预测的方法。我们描述了英国汉普郡非特异性胃肠病在线时空监测的应用。结果以超出概率图的形式呈现，P{R(x,t)> c 垂直条数据}，其中 R(x, t) 是 lambda(x, t) 的未观察到的随机分量的当前实现，c 是预先指定的阈值。这些地图会使用基于网络的报告系统根据每天的事件数据自动更新。版权所有 (c) 2005 John Wiley & Sons, Ltd.
We formulate the problem of on-line spatio-temporal disease surveillance in terms of predicting spatially and temporally localised excursions over a pre-specified threshold value for the spatially and temporally varying intensity of a point process in which each point represents an individual case of the disease in question. Our point process model is a non-stationary log-Gaussian Cox process in which the spatio-temporal intensity, lambda (x, t), has a multiplicative decomposition into two deterministic components, one describing purely spatial and the other purely temporal variation in the normal disease incidence pattern, and an unobserved stochastic component representing spatially and temporally localised departures from the normal pattern. We give methods for estimating the parameters of the model, and for making probabilistic predictions of the current intensity. We describe an application to on-line spatio-temporal surveillance of non-specific gastroenteric disease in the county of Hampshire, UK. The results are presented as maps of exceedance probabilities, P{R(x,t)> c vertical bar data}, where R(x, t) is the current realisation of the unobserved stochastic component of lambda(x, t) and c is a pre-specified threshold. These maps are updated automatically in response to each day's incident data using a web-based reporting system. Copyright (c) 2005 John Wiley & Sons, Ltd.