Point process methodology for on‐line spatio‐temporal disease surveillance

TLDR

On‑line spatio‑temporal disease surveillance seeks to predict localized excursions above a threshold in the intensity of a point process where each point represents an individual case. The study develops methods to estimate model parameters and generate probabilistic predictions of the current intensity. A non‑stationary log‑Gaussian Cox process is used, decomposing intensity into deterministic spatial and temporal components and a stochastic departure term, with parameters estimated and predictions produced via a web‑based system that updates daily maps for gastroenteric disease in Hampshire. The approach yields maps of exceedance probabilities, showing the likelihood that the stochastic component of intensity exceeds a pre‑specified threshold. © 2005 John Wiley & Sons, Ltd.

Abstract

Abstract 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, λ(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 λ(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.