Hadjichrysanthou Christoforos, Sharkey Kieran J
Department of Mathematical Sciences, University of Liverpool, Mathematical Sciences Building, Liverpool L69 7ZL, United Kingdom.
Department of Mathematical Sciences, University of Liverpool, Mathematical Sciences Building, Liverpool L69 7ZL, United Kingdom.
J Theor Biol. 2015 Jan 21;365:84-95. doi: 10.1016/j.jtbi.2014.10.006. Epub 2014 Oct 22.
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.
在根除流行病的资源有限的情况下，或者在我们试图尽量减少干预措施可能产生的不利影响的情况下，优化控制措施的效果至关重要。我们引入了一种新方法——疫情控制分析（ECA），以设计有效的针对性干预策略，减轻和控制感染在异构接触网络中的传播。我们在新开发的个体层面确定性易感-感染-易感（SIS）流行病学模型的背景下举例说明这种方法（我们也简要考虑其在易感-感染-移除（SIR）动态中的应用）。这提供了一种灵活的方式来系统地确定干预措施对人群中地方性感染的影响。个体根据其对感染水平的影响进行排名。排名最高的个体被优先进行针对性干预。许多先前的干预策略主要基于个体在网络中的位置（由各种局部和全局网络中心性度量描述）及其感染的可能性来确定优先级。将所提出策略的预测与各种模型和现实世界网络上广泛使用的针对性干预计划的预测进行比较，揭示了其效率和准确性。结果表明，针对中心个体或感染概率高的个体并不总是最佳策略。个体的重要性并非仅由网络结构决定，而是可能高度依赖于感染动态。ECA有效地捕捉了网络结构和感染动态之间的这种相互作用。
