论文标题
异质干预措施减少了Covid-19在实际流动数据上的模拟中的传播
Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data
论文作者
论文摘要
全世界已经引入了重大干预措施，以减缓SARS-COV-2病毒的扩散。人类运动的大规模封锁可以有效地减少差异，但它们的社会功能有效。我们表明，自然的人类运动在统计上是多种多样的，疾病的传播受到一小群活跃的个体和收集场地的显着影响。我们发现，集中在这些大多数流动人士和流行场所的干预措施降低了峰值感染率和受感染总人群的同时，同时保持了高度的社交活动水平。这些趋势始终存在于世界各地多个城市的不同规模，决议和方式的真实人类流动性数据的模拟中。该观察结果表明，与广泛的全面干预措施相比，基于人类流动性的网络效应而针对的更异质的策略为大流行控制和常规社交活动提供了更好的平衡。
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large-scale lockdowns of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.
