  Understanding individual decisions in a world where communications and
information move instantly via cell phones and the internet, contributes to the
development and implementation of policies aimed at stopping or ameliorating
the spread of diseases. In this manuscript, the role of official social network
perturbations generated by public health officials to slow down or stop a
disease outbreak are studied over distinct classes of static social networks.
The dynamics are stochastic in nature with individuals (nodes) being assigned
fixed levels of education or wealth. Nodes may change their epidemiological
status from susceptible, to infected and to recovered. Most importantly, it is
assumed that when the prevalence reaches a pre-determined threshold level, P*,
information, called awareness in our framework, starts to spread, a process
triggered by public health authorities. Information is assumed to spread over
the same static network and whether or not one becomes a temporary informer, is
a function of his/her level of education or wealth and epidemiological status.
Stochastic simulations show that threshold selection P* and the value of the
average basic reproduction number impact the final epidemic size
differentially. For the Erdos-Renyi and Small-world networks, an optimal choice
for P* that minimize the final epidemic size can be identified under some
conditions while for Scale-free networks this is not case.
