In recent years several new
infectious diseases have emerged: SARS, Avian
Influenza and now Swine-Associated Influenza. In
each case the challenge is how best to respond to
these emerging threats in the face of incomplete
information (concerning, for example, infectivity,
transmission and relative risk to the community).
Standard math-ematical models of disease propagation
do a bad job of dealing with this lack of
information as they focus on (unknown) properties of
the disease. Nonetheless, as new diseases emerge we
are faced with the challenge of how best to respond
and to protect public health, while not
over-reacting.
In this project we have applied
new mathematical models from the field of complexity
theory to the study of infectious diseases. These
new methods focus on the properties of the community
itself and incorporate whatever infor-mation
concerning the infectious agent is available. As
more information becomes available, the model can be
improved. The basic tenet of complexity theory is
that system of many simple, but interacting,
elements can behave collec-tively in rather suprising
and startling ways. To study these systems one must
consider both the individual components and the
ensemble behaviour of the entire system. In the
application of these methods to infectious diseases
we study how the fundamentally varied behaviour of
the individuals within a community affects the
spread of an infectious agent within that community.
We apply both extensive computational modelling and
techniques from statistical physics to describe the
collective behaviour of the system. |