title: The detection of spatially localised outbreaks in campylobacteriosis notification data.
abstract: This paper applies a Bayesian hierarchical model designed to identify potential outbreaks of campylobacteriosis from a background of sporadic cases. We assume that such outbreaks are characterized by spatially-localised periods of increased incidence. As well as calculating an outbreak probability for each potential disease cluster, the model simultaneously estimates the underlying spatial and temporal distribution of sporadic cases. The model is applied to notification data from a region of New Zealand for the period 2001-2007 and correctly identifies known outbreaks, whilst highlighting an appropriate number of potential outbreaks for further investigation. Using simulated data, we show that if additional epidemiological information is included in the construction of the model then it can outperform an established method.
authors: Molecular Epidemiology and Veterinary Public Health Laboratory, Hopkirk Research Institute, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand. s.e.f.spencer@warwick.ac.uk
authors: Simon E F
authors: SE
authors: Spencer
authors: Jonathan
authors: J
authors: Marshall
authors: Ruth
authors: R
authors: Pirie
authors: Donald
authors: D
authors: Campbell
authors: Nigel P
authors: NP
authors: French
mesh_terms: D001499:Bayes Theorem; D002169:Campylobacter Infections / Q000453:epidemiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D062665:Epidemiological Monitoring; D062306:Geography, Medical / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009520:New Zealand / Q000453:epidemiology; D011159:Population Surveillance; D062206:Spatial Analysis