[论文解读] COVID Lessons: Was there any way to reduce the negative effect of COVID-19 on the United States economy?

Mohammadreza Mahmoudi|arXiv (Cornell University)|Jan 2, 2022

COVID-19 Pandemic Impacts被引用 4

一句话总结

本文提出，针对高风险人群（尤其是老年人）实施有针对性的封锁政策，可能显著减少美国的新冠肺炎死亡人数和经济损失。通过将调整后的 SEQIER 模型拟合真实死亡数据，研究发现，此类策略可避免约 280,000 例死亡，并使国内生产总值（GDP）损失减少 1.5 个百分点，相较于实际结果有显著改善。

ABSTRACT

This paper aims to study the economic impact of COVID-19. To do that, in the first step, I showed that the adjusted SEQIER model, which is a generalization form of SEIR model, is a good fit to the real COVID-induced daily death data in a way that it could capture the nonlinearities of the data very well. Then, I used this model with extra parameters to evaluate the economic effect of COVID-19 through job market. The results show that there was a simple strategy that US government could implemented in order to reduce the negative effect of COVID-19. Because of that the answer to the paper's title is yes. If lockdown policies consider the heterogenous characteristics of population and impose more restrictions on old people and control the interactions between them and the rest of population the devastating impact of COVID-19 on people lives and US economy reduced dramatically. Specifically, based on this paper's results, this strategy could reduce the death rate and GDP loss of the United States 0.03 percent and 2 percent respectively. By comparing these results with actual data which show death rate and GDP loss 0.1 percent and 3.5 percent respectively, we could figure out that death rate reduction is 0.07 percent which means for the same percent of GDP loss executing optimal targeted policy could save 2/3 lives. Approximately, 378,000 persons dead because of COVID-19 during 2020, hence reducing death rate to 0.03 percent means saving around 280,000 lives, which is huge.

研究动机与目标

• 使用改进的流行病学模型评估新冠肺炎对美国经济的影响。
• 评估替代政策策略是否能够减轻大流行带来的健康与经济损失。
• 确定人口异质性（尤其是基于年龄的风险差异）是否可被利用以减少死亡率与经济冲击。
• 量化有针对性干预措施相较于广泛、无差别的封锁措施的潜在收益。
• 基于模型模拟，为未来大流行应对提供基于证据的政策建议。

提出的方法

• 将标准 SEIR 模型改进为调整后的 SEQIER 框架，以更好地捕捉每日新冠肺炎死亡数据中的非线性动态。
• 引入额外参数以建模人口异质性，特别是按年龄划分的传播率与易感性差异。
• 将模型校准至 2020 年美国每日死亡数据，以确保其经验有效性。
• 模拟了对老年人群加强限制、并最小化年轻与年长人群互动的替代政策情景。
• 通过就业市场指标将死亡率与感染率的降低与估算的 GDP 影响关联，量化经济影响。
• 将有针对性政策下的模型结果与实际观察到的死亡率与 GDP 损失进行比较。

实验结果

研究问题

• RQ1 针对高风险人群实施有针对性的封锁措施，能在多大程度上减少美国的新冠肺炎死亡人数？
• RQ2 聚焦于减少代际传播的政策将如何影响整体经济产出？
• RQ3 在最优政策响应与实际政策响应下，挽救生命与 GDP 损失之间的量化权衡是什么？
• RQ4 包含人口异质性的流行病学模型能否准确预测大流行期间的经济结果？
• RQ5 何种具体政策设计可实现单位经济成本下挽救最多的生命？

主要发现

• 调整后的 SEQIER 模型对真实美国每日死亡数据具有良好的拟合度，有效捕捉了疫情发展过程中的非线性趋势。
• 聚焦于老年人群的有针对性政策可将美国死亡率从 0.1% 降低至 0.03%，预计可避免约 280,000 例死亡。
• 此类策略可使 GDP 损失从 3.5% 降至 2%，相较于实际结果减少 1.5 个百分点。
• 在相同 GDP 损失水平下，有针对性的策略可避免实际政策下约三分之二的死亡人数。
• 模型表明，基于年龄的干预措施可使公共卫生结果与广泛经济中断脱钩。
• 结果表明，人口异质性是设计有效大流行应对策略的关键因素。