  A probabilistic approach to the epidemic evolution on realistic
social-contact networks allows for characteristic differences among subjects,
including the individual number and structure of social contacts, and the
heterogeneity of the infection and recovery rates according to age or medical
preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR)
model on social-contact networks, we evaluate the `infection load' or
`activation margin' of various control scenarios; by confinement, by
vaccination, and by their combination. We compare the epidemic burden for
subpopulations which apply competing or co-operative control strategies. The
simulation experiments are conducted on randomised social-contact graphs that
are designed to exhibit realistic person-person contact characteristics and
which follow near `homogeneous' or `block-localised' subpopulation spreading.
The scalarization method is used for the multi-objective optimization problem
in which both the infection load is minimized and the extent to which each
subpopulation's control strategy preference ranking is adhered to is maximized.
We obtain the compounded payoff matrices for two subpopulations which impose
contrasting control strategies, each according to their proper ranked control
strategy preferences. The Nash equilibria, according to each subpopulation's
compounded objective, and according to their proper ranking intensity, are
discussed. Finally, the interaction effects of the control strategies are
discussed and related to the type of spreading of the two subpopulations.

Social and Information Networks;Populations and Evolution