  This paper investigates optimization policies for resource distribution in
network epidemics using a model that derives from the classical Polya process.
The basic mechanics of this model, called the Polya network contagion process,
are based on a modified urn sampling scheme that accounts for both temporal and
spatial contagion between neighbouring nodes in a network. We present various
infection metrics and use them to develop two problems: one which takes place
upon initialization and one which occurs continually as the Polya network
process develops. We frame these problems as resource allocation problems with
fixed budgets, and analyze a suite of potential policies. Due to the complexity
of these problems, we introduce effective proxy measures for the average
infection rate in each case. We also prove that the two-sided infection-curing
game on the so-called expected network exposure admits a Nash equilibrium. In
both the curing and initialization scenarios, we introduce heuristic policies
that primarily function on the basis of limiting the number of targeted nodes
within a particular network setup. Simulations are run for mid-to-large scale
networks to compare performance of our heuristics to provably convergent
gradient descent algorithms run on the simplified proxy measures.
