Department of Mathematics, University of California Irvine, Irvine, CA 92697, United States.
Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA, 92697, United States.
Epidemics. 2021 Jun;35:100463. doi: 10.1016/j.epidem.2021.100463. Epub 2021 May 8.
Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a "peak and decay" pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.
非药物干预措施，如社交距离，迄今为止一直是减缓 SARS-CoV-2 传播的唯一手段。在美国，病毒传播第一波期间的严格社交距离导致了不同类型的感染动态。在一些州，如纽约，广泛的感染传播后，感染水平明显下降。在其他州，如加利福尼亚州，在严格的社交距离之前发生的感染传播较少，观察到不同的模式。没有明显的感染下降，而是出现了持久的高原，其特征是每日新感染水平相似。在这里，我们表明，如果由于社交距离措施而切断网络连接，网络模型（其中明确跟踪个人及其社交联系人）可以复制高原。原因是在具有二维空间结构的网络中，感染倾向于随时间呈二次方传播，但随着边缘被随机移除，感染沿着近乎一维的感染“走廊”传播，导致高原动力学。此外，我们表明，只有在干预措施开始足够早的情况下才会观察到高原动力学；晚期干预会导致“峰值和衰减”模式。有趣的是，预测高原动力学最终将过渡到感染下降阶段，而无需进一步增加社交距离措施。此外，模型表明，如果社会距离仅在动态过渡到下降阶段后才放宽，第二波的影响将显著降低。这里分析的网络模型使我们能够解释和调和美国各州在社交距离期间观察到的不同感染动态。
