  Structured epidemic models can be formulated as first-order hyperbolic PDEs,
where the "spatial" variables represent individual traits, called structures.
For models with two structures, we propose a numerical technique to approximate
$R_{0}$, which measures the transmissibility of an infectious disease and,
rigorously, is defined as the dominant eigenvalue of a next-generation
operator. Via bivariate collocation and cubature on tensor grids, the latter is
approximated with a finite-dimensional matrix, so that its dominant eigenvalue
can easily be computed with standard techniques. We use test examples to
investigate experimentally the behavior of the approximation: the convergence
order appears to be infinite when the corresponding eigenfunction is smooth,
and finite for less regular eigenfunctions. To demonstrate the effectiveness of
the technique for more realistic applications, we present a new epidemic model
structured by demographic age and immunity, and study the approximation of
$R_{0}$ in some particular cases of interest.
