  The SIR(D) epidemiological model is defined through a system of
transcendental equations, not solvable by elementary functions. In the present
paper those equations are successfully replaced by approximate ones, whose
solutions are given explicitly in terms of elementary functions, originating,
piece-wisely, from generalized logistic functions: they ensure {\em exact} (in
the numerical sense) asymptotic values, besides to be quite practical to use,
for example with fit to data algorithms; moreover they unveil a useful feature,
that in fact, at least with very strict approximation, is also owned by the
(numerical) solutions of the {\em exact} equations. The novelties in the work
are: the way the approximate equations are obtained, using simple, analytic
geometry considerations; the easy and practical formulation of the final
approximate solutions; the mentioned useful feature, never disclosed before.
The work's method and result prove to be robust over a range of values of the
well known non-dimensional parameter called {\em basic reproduction ratio},
that covers at least all the known epidemic cases, from influenza to measles:
this is a point which doesn't appear much discussed in analogous works.
