论文标题
接触网络模型匹配COVID-19的动力学扩展的动力学
Contact network models matching the dynamics of the COVID-19 spreading
论文作者
论文摘要
我们研究了在空间网络上的流行病扩散，其中两个节点与它们作为权力定律的距离连接起来的可能性。随着距离依赖性的指数的增长，模型网络从随机网络限制平稳过渡到常规晶格极限。我们表明，尽管保持平均接触次数恒定，但增加的指数通过使长距离连接的频率降低而阻碍了流行病的扩张。扩散动力学也受距离依赖性指数的影响，从指数增长到幂律增长。观察到的幂律增长与最近对Covid-19在许多国家中传播的经验数据的分析兼容。
We study the epidemic spreading on spatial networks where the probability that two nodes are connected decays with their distance as a power law. As the exponent of the distance dependence grows, model networks smoothly transition from the random network limit to the regular lattice limit. We show that despite keeping the average number of contacts constant, the increasing exponent hampers the epidemic spreading by making long-distance connections less frequent. The spreading dynamics is influenced by the distance-dependence exponent as well and changes from exponential growth to power-law growth. The observed power-law growth is compatible with recent analyses of empirical data on the spreading of COVID-19 in numerous countries.
