TY - JOUR

T1 - Point process methodology for on-line spatio-temporal disease surveillance.

AU - Rowlingson, Barry

AU - Diggle, Peter

AU - Su, T-L

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2005/8

Y1 - 2005/8

N2 - 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, (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|data}, where R(x,t) is the current realisation of the unobserved stochastic component of (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 © 2005 John Wiley & Sons, Ltd.

AB - 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, (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|data}, where R(x,t) is the current realisation of the unobserved stochastic component of (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 © 2005 John Wiley & Sons, Ltd.

KW - Cox process • disease surveillance • gastroenteric disease • Monte Carlo inference • spatial epidemiology • spatio-temporal point process

U2 - 10.1002/env.712

DO - 10.1002/env.712

M3 - Journal article

VL - 16

SP - 423

EP - 434

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 5

ER -