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.

翻译：近年来，网络科学与控制领域的研究表明，流行病传播过程（如COVID-19传播）与某些网络属性之间存在显著关联。本文系统研究了聚类系数和中心性度量（或节点影响力指标）两类网络属性如何影响病毒在无标度网络中的传播及疫情增长态势。研究结果可用于精准定位个体（网络中的节点），以实现《感染曲线平缓化》。所谓感染曲线平缓化，旨在降低医疗服务成本并减轻政府/当局的负担。我们的蒙特卡洛仿真结果表明：在同等连通性与隔离个体数量的条件下，聚类网络通常更易实现感染曲线平缓化。此外，基于距离的中心性度量（根据节点到其他节点的平均网络距离而非节点度数进行定位）是实施隔离/疫苗接种策略的更优选择。