such as "Introduction", "Conclusion"..etc
The WNV is a flavivirus which was isolated for the first time in
1937. Its name comes from the district of West Nile in Uganda. It was
detected in human, birds and mosquitoes in Egypt at the beginning of
the fifties, and has then been found in various European countries .
It is however only with the important 1996 human epidemic in Bucharest,
Romania, that WNV became a concern for public health. Moreover, there
is no specific treatment of the disease and no vaccine is yet available
for humans. The WNV was detected on the American continent in 1999 and
more specifically in New York . In Canada, WNV reached southern Ontario in 2001, while the first human cases were detected in August 2002 .
WNV made its appearance in Quebec in July 2002. The virus was then
propagated, like everywhere else, by the intermediary of mosquitoes and
birds. The expansion of this epizooty forced the Government of Quebec
to adopt an intervention plan which included in 2003 the implementation
of a multi-faceted surveillance system . This system brought together field data on human, avian and entomological infection and deaths.
While these monitoring activities were undertaken to better
understand the epidemiology of WNV and the level of risk it can
represent for the human population, they do not allow for forecasts of
the probable propagation of the virus on the territory. Such a
forecast, if it proved to be reliable, would allow public health
authorities to initiate preventative actions at the right time and
places and at the appropriate level of expected risk. Currently, one
main control activity is larvicide spraying in urban and rural settings
in order to reduce the population of mosquitoes infected with WNV.
However, it remains difficult to determine the at-risk zones on a
scientific basis and the efficacy of such measures has been challenged ,
not to mention their high cost and environmental impacts. The
identification of vulnerable zones and risk levels in due time remains
a significant challenge for public health management due to the
complexity of the phenomena related to the virus transmission.
Multi-agent geosimulation is an artificial intelligence modeling
approach which might be used to develop public health management tools
in order to anticipate the progression of the disease and to assess
various intervention scenarios. This approach makes it possible to
simulate the behaviours of thousands of agents in geo-referenced
virtual spaces. The MAGS System (Multi-Agent GeoSimulation) recently
developed by Dr. Moulin's Groupe de Recherche en Informatique Cognitive at
Laval University, can be used to create such simulations in virtual
environments generated with georeferenced data obtained from geographic
information systems (GIS). These agents are characterized by cognitive
capacities such as perception of the environment and its content,
autonomous navigation and reasoning .
Although one of the first applications of MAGS was related to the
simulation of crowd behaviours in urban environments, MAGS is a generic
platform allowing the simulation of several types of behaviours in
various geo-referenced virtual environments. For example, it has
already been used to simulate the behaviour of consumers visiting
shopping centers and firemen intervention plans to contain the
propagation of forest fires .
The main objective of the WNV-MAGS Project reported in this paper,
was to develop a system to simulate the behaviours and interactions of
populations of indicator birds and of mosquitoes involved in the
propagation and transmission of the WNV, taking into account the
characteristics of the geographic environment. This simulation takes
place in a virtual cartographic world representing a large territory
(southern part of the province of Quebec, Canada). The simulation also
takes into account various climatic scenarios and regimens of larvicide
In Section 2, we present an overview of the phenomena which are
linked to the spread of WNV. Then, we present the conceptual model
which was developed after setting some carefully chosen hypotheses.
Next, we present the geosimulation of the populations of interest,
using agents' roosts to represent the dynamics of the bird populations
and an intelligent density map to represent the populations of
mosquitoes. Some short-term climate scenarios and the calibration of
the system are also presented in this section. In Section 3, we present
a conclusion and some new work currently underway. In Section 4 we
briefly present the design method used to develop the system, including
the conceptual architecture and an overview of the mathematical model
formalizing the evolution of relevant populations. We also comment upon
the quality and availability of data used to feed the system. Finally,
we briefly present the implementation context of the system.
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