Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks
Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks
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DOI：
10.1016/j.jtbi.2014.10.006
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发表时间：
2015-01-21
影响因子：
2
通讯作者：
Sharkey, Kieran J.
中科院分区：
文献类型：
--
作者：
Hadjichrysanthou, Christoforos;Sharkey, Kieran J.
在根除流行病的资源有限的情况下，或者在我们努力尽量减少干预措施可能产生的不利影响的情况下，必须优化控制措施的效力。我们引入了一种新的方法，流行病控制分析（ECA），设计有效的有针对性的干预策略，以减轻和控制跨异构接触网络的感染传播。我们在一个新开发的个人水平的确定性易感-传染-易感（SIS）流行病学模型的背景下，这种方法（我们也简要地考虑应用易感-传染-删除（SIR）动态）。这为系统地确定干预措施对人群中地方性感染的影响提供了一种灵活的方式。根据个人对传染性水平的影响进行排名。排名最高的个人优先进行有针对性的干预。许多先前的干预策略主要根据个人在网络中的位置（由各种本地和全球网络中心性措施描述）及其传染性的机会来确定优先级。将所提出的策略与广泛使用的针对性干预方案在各种模型和现实网络上的预测进行比较，揭示了其效率和准确性。研究表明，针对中心个体或具有高感染概率的个体并不总是最佳策略。个体的重要性不仅取决于网络结构，还可能高度依赖于感染动态。网络结构和感染动态之间的这种相互作用被ECA有效地捕获。(C)2014作者爱思唯尔有限公司出版
In cases where there are limited resources for the eradication of an epidemic, or where we seek to minimise possible adverse impacts of interventions, it is essential to optimise the efficacy of control measures. We introduce a new approach, Epidemic Control Analysis (ECA), to design effective targeted intervention strategies to mitigate and control the propagation of infections across heterogeneous contact networks. We exemplify this methodology in the context of a newly developed individual-level deterministic Susceptible-Infectious-Susceptible (SIS) epidemiological model (we also briefly consider applications to Susceptible-Infectious-Removed (SIR) dynamics). This provides a flexible way to systematically determine the impact of interventions on endemic infections in the population. Individuals are ranked based on their influence on the level of infectivity. The highest-ranked individuals are prioritised for targeted intervention. Many previous intervention strategies have determined prioritisation based mainly on the position of individuals in the network, described by various local and global network centrality measures, and their chance of being infectious. Comparisons of the predictions of the proposed strategy with those of widely used targeted intervention programmes on various model and real-world networks reveal its efficiency and accuracy. It is demonstrated that targeting central individuals or individuals that have high infection probability is not always the best strategy. The importance of individuals is not determined by network structure alone, but can be highly dependent on the infection dynamics. This interplay between network structure and infection dynamics is effectively captured by ECA. (C) 2014 The Authors. Published by Elsevier Ltd.