International Computer Science Institute and Department of IEOR, University of California, Berkeley, Berkeley, CA, United States.
COVID-19 Policy Alliance, Cambridge, MA, United States.
Front Public Health. 2020 Jun 8;8:266. doi: 10.3389/fpubh.2020.00266. eCollection 2020.
In the face of elevated pandemic risk, canonical epidemiological models imply the need for extreme social distancing over a prolonged period. Alternatively, people could be organized into zones, with more interactions inside their zone than across zones. Zones can deliver significantly lower infection rates, with less social distancing, particularly if combined with simple quarantine rules and contact tracing. This paper provides a framework for understanding and evaluating the implications of zones, quarantines, and other complementary policies.
面对高传染性疾病的风险，传统的流行病学模型表明，需要在较长时间内实施极端的社会隔离措施。或者，可以将人们组织到不同的区域中，在区域内的互动比跨区域的互动更多。区域可以显著降低感染率，同时减少社会隔离，特别是如果结合简单的隔离规则和接触者追踪。本文提供了一个理解和评估区域、隔离区和其他补充政策影响的框架。
