Brethouwer Jan-Tino, van de Rijt Arnout, Lindelauf Roy, Fokkink Robbert
TU Delft, Delft Institute of Applied Mathematics, Netherlands.
European University Institute, Political and Social Sciences, Italy.
Infect Dis Model. 2021;6:36-45. doi: 10.1016/j.idm.2020.10.013. Epub 2020 Nov 16.
This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid- 19 across EU member states, German and Italian regions, and through New York City, providing some model validation. Results suggest that our proposed strategy may significantly reduce peak infection. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. These results suggest a tailored policy in which individuals who frequently travel to places where they interact with many people are offered greater protection, tracked more closely, and are regularly tested. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.
本文将网络理论中的经典见解——长距离连接推动疾病传播——重新应用于控制新冠疫情第二波传播的策略中。我们模拟了这样一种情景:首先对人群实施封锁,然后在将远距离传播保持在最低水平的同时部分解除封锁。模拟的传播模式类似于新冠疫情在欧盟成员国、德国和意大利各地区以及纽约市的当代分布情况,从而提供了一定的模型验证。结果表明,我们提出的策略可能会显著降低感染峰值。我们还发现,解封后的疫情爆发在更长时间内保持局部性,有助于地理上的控制。这些结果表明了一种针对性的政策,即对于经常前往与许多人有互动的地方的个人,给予更多保护、更密切地追踪并定期进行检测。这一政策可以作为一个简单合理的原则传达给公众:待在附近或接受检查。