Identifying space-time disease clusters

Acta Tropica
Rose D Baker

Abstract

A cluster of cases of disease that are close both in space and in time is suggestive of an infectious aetiology. We present statistical tests for space-time clusters of disease for the two situations where the population at risk is either known or unknown as a function of space and time. The tests are derived using standard statistical methodology from a simple mathematical model of disease spread, i.e. they are derived as score tests from a likelihood function in which the infection process is modelled as a point process whose intensity becomes greater near an infector. A problem for such tests is that, when investigating whether or not a disease may be of infectious origin, the space and time distances characterising closeness to an infection are very likely to be unknown. The proposed methodology copes with this difficulty in a statistically acceptable way, without requiring multiple tests whose interpretation would be doubtful. When the underlying population size is unknown, the test reduces to a modification of the Knox test. An example of its use is given as epidemiology, risk, space-time cluster, likelihood and Knox test.

References

Feb 15, 1984·International Journal of Cancer. Journal International Du Cancer·J McHardyG Giraldo
Apr 25, 2001·Biometrics·M Kulldorff, U Hjalmars
Apr 10, 2002·Journal of Public Health Medicine·E G Knox

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Citations

Dec 2, 2010·Phytopathology·P S Ojiambo, G J Holmes
Oct 1, 2010·Demography·Carl P SchmertmannJoseph E Potter
Sep 7, 2012·PLoS Neglected Tropical Diseases·Raphaëlle MétrasRichard G White
Dec 1, 2006·Annals of the New York Academy of Sciences·Vincent HerbreteauJean-Paul Gonzalez
Apr 13, 2012·Statistics in Medicine·Nicholas Malizia, Elizabeth A Mack
Jul 13, 2004·Acta Tropica·A J GrahamF M Danson
Jul 2, 2014·The American Journal of Tropical Medicine and Hygiene·Yi HuQingwu Jiang

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