  Networks of person-person contacts form the substrate along which infectious
diseases spread. Most network-based studies of the spread focus on the impact
of variations in degree (the number of contacts an individual has). However,
other effects such as clustering, variations in infectiousness or
susceptibility, or variations in closeness of contacts may play a significant
role. We develop analytic techniques to predict how these effects alter the
growth rate, probability, and size of epidemics and validate the predictions
with a realistic social network. We find that (for given degree distribution
and average transmissibility) clustering is the dominant factor controlling the
growth rate, heterogeneity in infectiousness is the dominant factor controlling
the probability of an epidemic, and heterogeneity in susceptibility is the
dominant factor controlling the size of an epidemic. Edge weights (measuring
closeness or duration of contacts) have impact only if correlations exist
between different edges. Combined, these effects can play a minor role in
reinforcing one another, with the impact of clustering largest when the
population is maximally heterogeneous or if the closer contacts are also
strongly clustered. Our most significant contribution is a systematic way to
address clustering in infectious disease models, and our results have a number
of implications for the design of interventions.
