  Our main goal is to examine the role of communities in epidemic spread in a
random graph model. More precisely, we consider a random graph model which
consists of overlapping complete graphs, representing households, workplaces,
school classes, and which also has a simple geometric structure. We study the
model's sensitivity to infection parameters and other tunable parameters of the
model, which might be helpful in finding efficient social distancing
strategies. We also quantitatively compare different vaccination strategies to
see which order is the best to defend the most vulnerable groups or the
population in general, and how important it is to gather and use information on
the position of infected individuals in the network.
