  The compartmental models used to study epidemic spreading often assume the
same susceptibility for all individuals, and are therefore, agnostic about the
effects that differences in susceptibility can have on epidemic spreading. Here
we show that--for the SIS model--differential susceptibility can make networks
more vulnerable to the spread of diseases when the correlation between a node's
degree and susceptibility are positive, and less vulnerable when this
correlation is negative. Moreover, we show that networks become more likely to
contain a pocket of infection when individuals are more likely to connect with
others that have similar susceptibility (the network is segregated). These
results show that the failure to include differential susceptibility to
epidemic models can lead to a systematic over/under estimation of fundamental
epidemic parameters when the structure of the networks is not independent from
the susceptibility of the nodes or when there are correlations between the
susceptibility of connected individuals.
