  Recent studies in network science and control have shown a meaningful
relationship between the epidemic processes (e.g., COVID-19 spread) and some
network properties. This paper studies how such network properties, namely
clustering coefficient and centrality measures (or node influence metrics),
affect the spread of viruses and the growth of epidemics over scale-free
networks. The results can be used to target individuals (the nodes in the
network) to \textit{flatten the infection curve}. This so-called flattening of
the infection curve is to reduce the health service costs and burden to the
authorities/governments. Our Monte-Carlo simulation results show that clustered
networks are, in general, easier to flatten the infection curve, i.e., with the
same connectivity and the same number of isolated individuals they result in
more flattened curves. Moreover, distance-based centrality measures, which
target the nodes based on their average network distance to other nodes (and
not the node degrees), are better choices for targeting individuals for
isolation/vaccination.
