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Date: July 2, 2004 (Friday)
Time: 12:30 to 14:00 (Light lunch at 12:30, Seminar begins at 12:45)
Venue: Mrs Chen Yang Foo O Telemedicine Centre, 2/F, Academic and
Administration Block, Faculty of Medicine Building, 21 Sassoon Road,
Pokfulam
Abstract:
Both literally and metaphorically there is no future in disease
surveillance. The best-case scenario for surveillance is that it
provides data that facilitates the analysis of the 'current' situation;
however, more often it only provides a historical synopsis of disease
in the community. This is so unsatisfactory that it continues on
momentum only, "we do what we've always done".
Disease surveillance is currently
an observed data endeavour. It collects data; it adds error to data;
it stores data; it adds more error to data; it manipulates data,
again more error is added to data; it sometimes even analyses data
to produce information. The data are always given to decision-makers
with the caveat, "due to the poor quality of data¡K"
The future lies with disease modelling
that is designed to answer explicit questions. The role of 'disease
surveillance' will be to provide input into these models. The model
output from surveillance systems will answer predefined questions,
with accompanying measures of uncertainty and the ability to directly
measure the system's success in answering the required questions.
Bio-sketch:
Chris has been with the Population & Environmental Health Programme
at the Institute for Environmental Science and Research since January
2003, where he leads the research and development programme in surveillance
systems. He was with the Public Health Intelligence Group at the
New Zealand Ministry of Health (1997-2003), where he led the development
of a national Health GeoInformatics programme. Prior to that he
lectured in the Spatial Analysis and GIS programme of the Department
of Tropical Environmental Studies and Geography, James Cook University,
North Queensland, Australia (1993-1997). Chris has spent the last
15 years working with observed and simulated data in the fields
of epidemiology, climatology, remote sensing, natural hazards and
many areas of environmental science. Disease surveillance system
data is worse than any other type of data he's worked with. He'll
present a case for the demise of disease surveillance and the ascendancy
of disease modelling.
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