RAPID Choices under Short-Term Threats and Behavioral Response to Social Distancing in the COVID-19 Pandemic

COVID-19 大流行中短期威胁下的快速选择以及对社交距离的行为反应

• 批准号: 2031841
• 负责人: Ricardo Daziano
• 金额: $ 10.27万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031841&HistoricalAwards=false
• 关键词: RAPID; Choices; under; Short; Term

Social-interaction restrictions have to be respected to contain the spread of highly contagious diseases such as COVID-19. Ensuring compliance to strict health interventions, however, needs better understanding of individual decisions under risk. Until a vaccine is developed, policy makers not only need to find the best incentives for people to avoid physical proximity, but they also need to create plans for relaxing social distancing in the near future. Behavioral forecasts from the models developed in this project help to guide decisions in both cases. Specifically, this project provides answers to several policy-relevant questions such as: What changes in social behavior are people adopting in response to the disruptions created by the COVID-19 outbreak? Who is more likely to respect health guidelines, including social distancing protocols? What are safety/risk perceptions of physical proximity in public spaces (pharmacies, grocery stores, take-outs at restaurants) and how are these perceptions shaping daily choices? How to create incentives for individuals to sustain social-distancing? How are COVID-19 disruptions causing losses in individual welfare, and how can analysts derive metrics to value such losses? In this project, the research team adopts and significantly adapts crowding research tools and methods in retail and transportation studies to analyze social distancing behaviors as preventive action against threats to health. An innovate virtual-reality-based online survey with choice experiments on social distancing collects time-sensitive behavioral data. The data are modeled using micro-econometric discrete-continuous choice models with structural equations for attitudinal components and heavy-tailed error distributions for preference shocks. Unlike standard thin-tailed distributions, error terms that exhibit heavy tails not only generalize standard assumptions but also address decision-uncertainty behavior. Flexible decision rules under risk are integrated into the discrete-continuous choice models that represent time-use scheduling during total and partial lock-downs. Ultimately, research outcomes from this study provide guidance to policy-makers for how to best implement measures such as social distancing and quarantines in order to control major epidemics, and then how to best phase out these measures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了遏制COVID-19等高度传染性疾病的传播，必须遵守社交互动限制。然而，要确保严格的卫生干预措施得到遵守，就需要更好地了解面临风险的个人决定。在开发出疫苗之前，政策制定者不仅需要找到最好的激励措施，让人们避免身体接触，而且还需要制定在不久的将来放松社交距离的计划。本项目中开发的模型的行为预测有助于指导这两种情况下的决策。具体而言，该项目为几个与政策相关的问题提供了答案，例如：为了应对COVID-19疫情造成的破坏，人们正在采取哪些社会行为变化？谁更有可能尊重健康指南，包括社交距离协议？在公共场所（药店、杂货店、餐馆外卖），人们对身体接近的安全/风险有什么看法？这些看法如何影响人们的日常选择？如何激励个人保持社交距离？COVID-19的中断如何造成个人福利损失，分析师如何得出衡量这些损失的指标？在本项目中，研究团队采用并显著调整了零售和交通研究中的拥挤研究工具和方法，以分析社会距离行为作为对健康威胁的预防措施。一项创新的基于虚拟现实的在线调查与社交距离的选择实验收集了时间敏感的行为数据。数据使用微观计量离散-连续选择模型建模，该模型具有态度成分的结构方程和偏好冲击的重尾误差分布。与标准的细尾分布不同，表现出重尾的误差项不仅概括了标准假设，而且还解决了决策不确定性行为。将风险下的柔性决策规则集成到表示全锁定和部分锁定时的时间使用调度的离散-连续选择模型中。最终，本研究的研究成果为政策制定者提供了指导，指导他们如何最好地实施社会距离和隔离等措施，以控制重大流行病，然后如何最好地逐步淘汰这些措施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Willingness to delay charging of electric vehicles

电动汽车推迟充电的意愿

• 发表时间: 2020
• 期刊: 25th Annual Conference of the European Association Environmental and Resource Economists
• 作者: Daziano, R.A.

How does self-assessed health status relate to preferences for cycling infrastructure? A latent class and latent variable approach

自我评估的健康状况与自行车基础设施的偏好有何关系？

• DOI: 10.1007/s11116-022-10266-z
• 发表时间: 2022
• 期刊: Transportation
• 影响因子: 4.3
• 作者: Rossetti, Tomás;Daziano, Ricardo
• 通讯作者: Daziano, Ricardo

Social distancing and store choice in times of a pandemic

疫情期间的社交距离和商店选择

• DOI: 10.1016/j.jretconser.2021.102860
• 发表时间: 2021-12-23
• 期刊: Journal of Retailing and Consumer Services
• 影响因子: 10.4
• 作者: Rossetti T;Yoon SY;Daziano RA
• 通讯作者: Daziano RA

On Assignment to Classes in Latent Class Logit Models

关于潜在类别 Logit 模型中的类别分配

• DOI: 10.1177/03611981221121266
• 发表时间: 2023
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Wu, Wangwei;Daziano, Ricardo A.
• 通讯作者: Daziano, Ricardo A.