  Understanding the impact of network clustering and small-world properties on
epidemic spread can be crucial in developing effective strategies for managing
and controlling infectious diseases. Particularly in this work, we study the
impact of these network features on targeted intervention (e.g., self-isolation
and quarantine). The targeted individuals for self-isolation are based on
centrality measures and node influence metrics. Compared to our previous works
on scale-free networks, small-world networks are considered in this paper.
Small-world networks resemble real-world social and human networks. In this
type of network, most nodes are not directly connected but can be reached
through a few intermediaries (known as the small-worldness property). Real
social networks, such as friendship networks, also exhibit this small-worldness
property, where most people are connected through a relatively small number of
intermediaries. We particularly study the epidemic curve flattening by
centrality-based interventions/isolation over small-world networks. Our results
show that high clustering while having low small-worldness (higher shortest
path characteristics) implies flatter infection curves. In reality, a flatter
infection curve implies that the number of new cases of a disease is spread out
over a longer period of time, rather than a sharp and sudden increase in cases
(a peak in epidemic). In turn, this reduces the strain on healthcare resources
and helps to relieve the healthcare services.
