论文标题
预症状传播对接触网络中流行病扩散的影响：动态消息分析
Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis
论文作者
论文摘要
结合了症状前传播的传染病对于监测，模型，预测和包含是挑战性的。我们通过研究基于动态消息通话方法的分析框架，在任意网络实例上研究随机易感性感染的模型的变体来解决这种情况。该框架提供了对静态和接触网络上差异的概率演变的良好估计，相对于基于个体的平均场方法提供了明显提高的准确性，而与数值模拟相比，计算成本要低得多。它促进了流行阈值的推导，这些阈值是分隔参数状态的相边界，其中可以有效地与无法有效的感染所含有感染。这些通过拓扑（减少接触）和感染参数变化（例如，社交距离和戴着口罩）对不同的遏制策略具有明显的影响，与最近的Covid-19-19s有关。
Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.
