  We present a quenched mean-field (QMF) theory for the dynamics of the
susceptible-infected-susceptible (SIS) epidemic model on complex networks where
dynamical correlations between connected vertices are taken into account by
means of a pair approximation. We present analytical expressions of the
epidemic thresholds in the star and wheel graphs and in random regular
networks. For random networks with a power law degree distribution, the
thresholds are numerically determined via an eigenvalue problem. The pair and
one-vertex QMF theories yield the same scaling for the thresholds as functions
of the network size. However, comparisons with quasi-stationary simulations of
the SIS dynamics on large networks show that the former is quantitatively much
more accurate than the latter. Our results demonstrate the central role played
by dynamical correlations on the epidemic spreading and introduce an efficient
way to theoretically access the thresholds of very large networks that can be
extended to dynamical processes in general.
