Efficient sentinel surveillance strategies for preventing epidemics on networks

Ewan Colman*, Petter Holme, Hiroki Sayama, Carlos Gershenson


研究成果: Article査読

11 被引用数 (Scopus)


Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.

ジャーナルPLoS Computational Biology
出版ステータスPublished - 2019

ASJC Scopus subject areas

  • 生態、進化、行動および分類学
  • モデリングとシミュレーション
  • 生態学
  • 分子生物学
  • 遺伝学
  • 細胞および分子神経科学
  • 計算理論と計算数学


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