识别人类对外部风险的延迟反应:在流行病期间对流动性变化的经济学分析
Gaofei Zhang1, Ann Osi2, Navid Ghaffarzadegan2
1Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA.
BMC medical research methodology
|October 30, 2025
概括
在COVID-19死亡后,人类流动性显著下降,延迟时间因州而异. 这项研究引入了一种混合的ARDL-Erlang模型,以更好地了解这些对健康风险的延迟行为反应.
科学领域:
- 流行病学 流行病学
- 行为科学 行为科学
- 计量经济学 计量经济学
背景情况:
- 人类对风险的行为反应往往会延迟,这给准确的建模带来了挑战.
- 估计延迟反应的现有方法存在局限性,包括刚性假设或难以集成到模拟模型中.
- 建议采用一种新的混合自回归分布式滞后 (ARDL) - Erlang 方法,提供行为反应的可解释摘要.
研究的目的:
- 开发和应用混合ARDL-Erlang模型来估计人类对健康风险的延迟行为反应.
- 量化COVID-19死亡对美国各县人类流动性的影响.
- 分析这些行为反应的时间和规模的异质性.
主要方法:
- 应用标准面板ARDL模型来估计过去COVID-19死亡和流动性对当前流动性的影响.
- 将ARDL模型转换为以往死亡为重点的无限分布式滞后 (IDL) 模型.
- 将过去死亡的聚合系数与Erlang分布相匹配,以总结延迟长度和形状参数.
主要成果:
- 每周人均COVID-19死亡人数增加1个SD与全国长期人类流动性减少0.46个SD有关.
- 全国延迟分配是在第一阶段的Erlang分配之后进行的,平均延迟约为3.2周.
- 观察到州一级的显著异质性,延迟订单范围从第一到第三,平均延迟时间从2到18周.
结论:
- 混合ARDL-Erlang模型提供了一种新且有效的方法来估计人类对健康风险的延迟反应.
- 调查结果显示,美国各州对COVID-19死亡的行为反应存在很大差异,强调需要量身定制的公共卫生政策.
- 这种方法可用于在其他与健康相关的环境中识别延迟的人类行为.
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