  We propose a generalization of the adaptive N-Intertwined Mean-Field
Approximation (aNIMFA) model studied in \emph{Achterberg and Sensi}
\cite{achterbergsensi2022adaptive} to a heterogeneous network of communities.
In particular, the multigroup aNIMFA model describes the impact of both local
and global disease awareness on the spread of a disease in a network. We obtain
results on existence and stability of the equilibria of the system, in terms of
the basic reproduction number~$R_0$. Under light constraints, we show that the
basic reproduction number~$R_0$ is equivalent to the basic reproduction number
of the NIMFA model on static networks. Based on numerical simulations, we
demonstrate that with just two communities periodic behaviour can occur, which
contrasts the case with only a single community, in which periodicity was ruled
out analytically. We also find that breaking connections between communities is
more fruitful compared to breaking connections within communities to reduce the
disease outbreak on dense networks, but both strategies are viable to networks
with fewer links. Finally, we emphasise that our method of modelling adaptivity
is not limited to SIS models, but has huge potential to be applied in other
compartmental models in epidemiology.
