Current mathematical modeling methods for the spreading
of infectious diseases are too simplified and do not scale well. We present
the Simulator of Epidemic Evolution in Complex Networks (SEECN),
an efficient simulator of detailed individual-based models by parameterizing
separate dynamics operators, which are iteratively applied to
the contact network. We reduce the network generator’s computational
complexity, improve cache efficiency and parallelize the simulator. To
evaluate its running time we experiment with an HIV epidemic model
that incorporates up to one million homosexual men in a scale-free network,
including hierarchical community structure, social dynamics and
multi-stage intranode progression. We find that the running times are
feasible, on the order of minutes, and argue that SEECN can be used to
study realistic epidemics and its properties experimentally, in contrast
to defining and solving ever more complicated mathematical models as
is the current practice.
