2005
DOI: 10.1002/env.712
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Point process methodology for on‐line spatio‐temporal disease surveillance

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, o… Show more

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Cited by 147 publications
(185 citation statements)
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“…A salient feature is that no calls were registered during the period September 13-30 with recording resuming on October 1st, 2001. For further details, see Diggle et al (2005).…”
Section: Extensions and Applicationmentioning
confidence: 99%
See 2 more Smart Citations
“…A salient feature is that no calls were registered during the period September 13-30 with recording resuming on October 1st, 2001. For further details, see Diggle et al (2005).…”
Section: Extensions and Applicationmentioning
confidence: 99%
“…As in Brix and Diggle (2001) and Diggle et al (2005), we assume that calls are made according to a log-Gaussian Cox process (Møller et al, 1998) and approximate the rate of calls during the ith day of the two-month period by…”
Section: Extensions and Applicationmentioning
confidence: 99%
See 1 more Smart Citation
“…Diggle et al (2004) use a spatio-temporal Cox point process methodology based on the counts in subregions. Olson et al (2005) and Forsberg et al (2006) assess possible disease clusters using M-statistics based on the distribution of pairwise distances between cases.…”
Section: Related Literaturementioning
confidence: 99%
“…Statistical procedures for analysing point process realizations can be found in books ( [6]; [22]; [12]; [19]) and a lot of papers deal with applications in special situations (e.g. [7]; [11]; [4]; [25]). Some studies are based on counts of events in sampling units ( [5]), and some others on event spatial positions or occurrence dates ( [25]), and also distance sampling ( [16]).…”
Section: Introductionmentioning
confidence: 99%