评估抗击COVID-19的针对性遏制政策

Ariadne Checo, F. Grigoli, J. Mota
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引用次数: 3

摘要

为应对COVID-19疫情而全面封锁的巨大经济成本,加上不同年龄组的死亡率差异,导致对非歧视性遏制措施的质疑。在本文中,我们提供了一个有针对性的遏制方法的评估。我们提出了一个sir宏观模型,该模型考虑了死亡率和接触率方面的异质性因素,并且政府最优地禁止人们工作。我们发现,在有针对性的政策下,与一揽子政策相比,最佳遏制措施覆盖的人口比例更大,持续时间更长。与一揽子政策相比,有针对性的做法可以减少死亡人数。然而,这不是万灵药:在这种方法下,经济衰退更大,因为遏制政策适用于更大比例的人,持续时间更长,群体免疫实现得更晚。此外,我们发现,低风险和高风险个体之间的互动增加,有效地降低了有针对性的遏制方法的好处。
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Assessing Targeted Containment Policies to Fight COVID-19
Abstract The large economic costs of full-blown lockdowns in response to COVID-19 outbreaks, coupled with heterogeneous mortality rates across age groups, led to question non-discriminatory containment measures. In this paper we provide an assessment of the targeted approach to containment. We propose a SIR-macro model that allows for heterogeneous agents in terms of mortality rates and contact rates, and in which the government optimally bans people from working. We find that under a targeted policy, the optimal containment reaches a larger portion of the population than under a blanket policy and is held in place for longer. Compared to a blanket policy, a targeted approach results in a smaller death count. Yet, it is not a panacea: the recession is larger under such approach as the containment policy applies to a larger fraction of people, remains in place for longer, and herd immunity is achieved later. Moreover, we find that increased interactions between low- and high-risk individuals effectively reduce the benefits of a targeted approach to containment.
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