Mathematical modelling of the spread of infectious diseases is a well established field with high practical importance. Underlying most analytical approaches is the assumption of "perfect mixing", that is the idea that the spatial structure of the population can be neglected. This assumption is crucial to the solvability of the models, but can be dropped when using computational models instead of analytical approaches. Using methods from Artificial Life, we investigate under which conditions the perfect mixing assumption becomes a good approximation to describe the spread of vector borne disease in a population with spatial structure
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