一种减轻病毒攻击影响的启发式优化方法
A heuristic optimization method for mitigating the impact of a virus attack
摘要：
在病毒爆发之前或期间采取预防措施可以大大减少感染人数。为了减轻病毒对其他人群的影响，哪些人应该接种疫苗的问题在文献中得到了相当多的关注。病毒在人群中传播的动态通常表示为在复杂网络中传播的信息。通常提出的策略，以确定哪些节点被选择免疫往往只涉及一个中心性措施的时间，而往往网络的拓扑结构似乎表明，一个单一的度量是不足以捕捉一个节点的影响力完全。在这项工作中，我们提出了一种通用的方法，基于遗传算法（GA），它不依赖于任何中心性措施，在其搜索过程中，但只利用这种类型的信息，缩小搜索空间。个体的适应度被定义为SIR动态下病毒感染的估计预期数量。所提出的方法进行评估的两个接触网络：Goodreau的假梅萨高中和美国航空运输网络。GA方法管理优于最常见的策略的基础上，一个单一的度量的航空运输网络和它的性能是与高中网络的最佳性能策略。
英文摘要：
Taking precautions before or during the start of a virus outbreak can heavily reduce the number of infected. The question which individuals should be immunized in order to mitigate the impact of the virus on the rest of population has received quite some attention in the literature. The dynamics of the of a virus spread through a population is often represented as information spread over a complex network. The strategies commonly proposed to determine which nodes are to be selected for immunization often involve only one centrality measure at a time, while often the topology of the network seems to suggest that a single metric is insufficient to capture the influence of a node entirely. In this work we present a generic method based on a genetic algorithm (GA) which does not rely explicitly on any centrality measures during its search but only exploits this type of information to narrow the search space. The fitness of an individual is defined as the estimated expected number of infections of a virus following SIR dynamics. The proposed method is evaluated on two contact networks: the Goodreau's Faux Mesa high school and the US air transportation network. The GA method manages to outperform the most common strategies based on a single metric for the air transportation network and its performance is comparable with the best performing strategy for the high school network.
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