论文标题
社交网络控制引起的流行振荡：不连续的情况
Epidemic oscillations induced by social network control: the discontinuous case
论文作者
论文摘要
引入诸如社会距离和锁定之类的遏制措施可以抑制流行病的传播。然而，当这种措施放松时，可能会发生新的流行波浪和感染周期。在这里，我们在人口感染状态与其社交网络的结构之间存在反馈的情况下，在隔间化流行模型中探索了这个问题，以进行不连续控制。我们表明，在随机图中，仅通过重新归一化的有效感染率来捕获遏制度量的影响，而这些感染率解释了网络的分支比率的变化。在我们的简单环境中，可以使用零件平均近似值来得出流行波及其长度的分析公式。该模型的一种不完美信息的变体用于模拟巴斯克国家和伦巴第近期的Covid-19流行病的数据，在那里我们估计锁定过程中社交网络中断的程度，并表征动态吸引子。
Epidemic spreading can be suppressed by the introduction of containment measures such as social distancing and lock downs. Yet, when such measures are relaxed, new epidemic waves and infection cycles may occur. Here we explore this issue in compartmentalized epidemic models on graphs in presence of a feedback between the infection state of the population and the structure of its social network for the case of discontinuous control. We show that in random graphs the effect of containment measures is simply captured by a renormalization of the effective infection rate that accounts for the change in the branching ratio of the network. In our simple setting, a piece-wise mean-field approximations can be used to derive analytical formulae for the number of epidemic waves and their length. A variant of the model with imperfect information is used to model data of the recent covid-19 epidemics in the Basque Country and Lombardy, where we estimate the extent of social network disruption during lock downs and characterize the dynamical attractors.
