分段隔离政策：管理传染病遏制与隔离社会成本之间的权衡
Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine
AI总结：
本研究提出分段隔离政策，通过优化接触者追踪范围和隔离期以最小化感染与隔离天数，利用智能体模拟和进化算法证明其比统一政策更有效且可持续。
摘要：
截至2021年底，COVID-19已蔓延至230多个国家，造成超过540万人死亡。为遏制其传播，许多国家实施了非药物干预措施，特别是接触者追踪和自我隔离政策。然而，这些措施带来了显著的社会成本，凸显了对尽量减少经济和社会活动中断的更可持续方法的需求。本研究探讨了一种分段隔离政策，对不同的群体分段应用不同的隔离措施，以更好地平衡遏制效益与成本。不同群体（如学生与在职成年人）具有不同的社会活动模式，对疾病传播构成不同风险。我们从接触者追踪范围和隔离期两个维度定义分段隔离政策，并为每个分段优化这些参数，以最小化总感染病例数和隔离天数。使用基于智能体的流行病模拟和进化算法推导帕累托前沿，我们证明分段政策比统一政策更有效，特定分段可从定制措施中受益。研究结果支持分段隔离作为一种可行、高效且可持续的方法，为未来大流行中的公共卫生政策提供了有价值的框架。
英文摘要：
By the end of 2021, COVID-19 had spread to over 230 countries, with over 5.4 million deaths. To contain its spread, many countries implemented non-pharmaceutical interventions, notably contact tracing and self-quarantine policies. However, these measures came with significant social costs, highlighting the need for more sustainable approaches that minimize disruptions to economic and societal activities. This research explores a segmentized quarantine policy, applying different quarantine measures for various population segments to better balance the benefits and costs of containment. Different groups, like students versus working adults, have distinct societal activity patterns, posing varied risks for disease spread. We define segmentized quarantine policy across two dimensions-contact tracing range and quarantine period-and optimize these parameters for each segment to minimize total infection cases and quarantine days. Using an Agent-Based Epidemic Simulation and an Evolutionary Algorithm to derive the Pareto front, we demonstrate that segmentized policies can be more effective than uniform policies, with specific segments benefiting from tailored measures. The findings support segmentized quarantine as a viable, efficient, and sustainable approach, offering a valuable framework for public health policy in future pandemics.
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