Everyone is welcome to attend the following talk:
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Friday 9th June 2000, 2pm
Room 6E2.1, University of Bath
Space-time prediction for on-line disease surveillance
Peter Diggle (Medical Statistics Unit, Lancaster University)
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For further details, see our Web site
http://www.stats.bris.ac.uk/~guy/Avon/
or contact Nicky Welton ([log in to unmask], Tel:
(0117) 965 6261 ext. 3227).
Location maps for Bath University can be
obtained from http://www.bath.ac.uk/opendays/maps.htm
Abstract:
~~~~~~~~~
*** Space-time prediction for on-line disease
*** surveillance
Peter Diggle, Anders Brix and Julia Kelsall
(Medical Statistics Unit, Lancaster University)
The work in this talk was motivated by the following
problem in environmental epidemiology, which is the subject
of a three-year collaborative research project between
Lancaster University and the Southampton Public Health
Laboratory Service. Individual cases of acute
gastro-infection within a health authority region are
indexed by the residential location of the patient and the
date of reporting of symptoms. These data are held
centrally and updated daily. In addition, the approximate
spatial distribution of the population at risk is known
from census data. The objective is to develop an automatic
surveillance system in order to identify and quantify
anomalous patterns of incidence which could indicate
spatially localised changes in disease risk. Identified
anomalies would then be subject to further investigations
of various kinds, including refinement of the diagnosis of
individual cases through pathological analysis, with a view
to detecting common causes of any genuine outbreaks.
Our general approach to this problem is to build a
space-time stochastic model for disease incidence and to
use the model to construct a predictive distribution for
the current value of the risk in the neighbourhood of an
arbitrary location. Our basic modelling assumption is that
the point process of disease incidence is a log-Gaussian
Cox process, i.e. a Poisson process whose space-time
intensity function is l(x,t) = exp{Z(x,t)} where Z(x,t) is
a Gaussian process.
In the talk, I will describe the exploratory analysis of
historical data using kernel smoothing methods, leading to
a detailed formulation of the point process model. I will
then discuss associated methods for estimating model
parameters and for identifying changes in the underlying
space-time intensity.
The full development and implementation of an on-line
surveillance system will involve many statistical modelling
issues concerned with both spatial and non-spatial aspects
of the problem. For example, one of many such issues is the
need to adjust for different reporting rates amongst
general practitioners and other health care providers
which, if undetected, could induce spurious local peaks in
incidence.
Acknowledgement: This work is supported financially by the
UK Department of Health.
----------------------------------------
Dr. Nicky Welton,
Office 2P10, CSM,
University of the West of England,
Bristol. BS16 1QY.
Email: [log in to unmask]
Tel: (0117) 965 6261 ext 3227
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