  In this article we mainly extend the deterministic model developed in [10] to
a stochastic setting. More precisely, we incorporated randomness in some
coefficients by assuming that they follow a prescribed stochastic dynamics. In
this way, the model variables are now represented by stochastic process, that
can be simulated by appropriately solve the system of stochastic differential
equations. Thus, the model becomes more complete and flexible than the
deterministic analogous, as it incorporates additional uncertainties which are
present in more realistic situations. In particular, confidence intervals for
the main variables and worst case scenarios can be computed.
