  There are many methods to estimate the quasi-stationary infected fraction of
the SIS process on (random) graphs. A challenge is to adequately incorporate
correlations, which is especially important in sparse graphs. Methods typically
are either significantly biased in sparse graphs, or computationally very
demanding already for small network sizes. In this paper we present a new
method to determine the infected fraction in sparse graphs, which we test on
Erd\H{o}s-R\'enyi graphs. Our method does take into account correlations and
gives accurate predictions. At the same time, computations are very feasible
and can easily be done even for large networks.
