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Abstract
In many parts of the world Echinococcus granulosus is a widespread infection in sheep
and dogs with a consequential spill over into the human population. In the past, mathematical
models have been derived to define the transmission dynamics of this parasite, principally
in the sheep-dog life cycle. These models have characterized the cycles of infection
as lacking in density dependent constraints in both the definitive or intermediate
hosts. This suggested that there was little, if any, induced host immunity by the
parasite in either host in natural infections. However, recent evidence from both
Tunisia and Kazakhstan, where young dogs are the most heavily parasitised, suggests
the possibility of significant definitive host immunity. This may have an effect on
the control effort needed to destabilize the parasite. A preliminary computer simulation
model (based on an Excel spreadsheet) to attempt to predict the results of a control
programme has been written. This demonstrates that there could be significantly different
results if there is indeed protective immunity in the dog than in the absence of immunity.
In the former the parasite needs a greater control effort to push the parasite towards
extinction than in the latter. The computer simulation is based on a mathematical
model of the parasite's life cycle and is flexible so that different values of parameters
can be used in different situations where the transmission of the parasite may be
at different levels. Because of the flexibility of the computer simulation it is anticipated
that this programme can be applied in most situations, although initial parameters
for a particular location or strain of the parasite will have to be first predetermined
with base line field surveys and possibly experimental infections. The programme also
has an additional flexibility to enable simulations if some parameters cannot be accurately
estimated through Monte-Carlo techniques. In the latter situation, worst and best
case scenarios can be estimated and likely frequency distributions of the unknown
parameters can be included in the model.