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

基本信息

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

项目摘要

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.

必须尊重社会交往限制，以包含高度传染性疾病（例如Covid-19）的传播。但是，确保遵守严格的健康干预措施需要更好地了解风险中的个人决策。在开发疫苗之前，决策者不仅需要为人们避免身体接近的最佳激励措施，而且还需要制定计划在不久的将来放松社会疏远的计划。在本项目中开发的模型的行为预测有助于指导两种情况下的决策。具体来说，该项目为几个与政策相关的问题提供了答案，例如：人们为响应Covid-19爆发造成的中断而采用社会行为的哪些变化？谁更有可能尊重包括社会疏远协议在内的健康准则？公共场所（药房，杂货店，餐馆外卖店）对身体接近的安全/风险感知是什么？这些看法如何塑造日常选择？如何为个人创造激励措施来维持社会持续存在吗？ COVID-19如何导致个人福利造成损失，分析师如何得出指标来重视此类损失？在该项目中，研究团队采用并大量适应了零售和运输研究中的拥挤研究工具和方法，以分析社会疏远行为，作为预防对健康威胁的预防行动。一项基于虚拟现实的创新在线调查，其选择实验有关社会距离的实验收集了时间敏感的行为数据。数据是使用具有态度组件的结构方程的微孔分散选择模型对数据进行建模的，用于偏好冲击的重型误差分布。与标准的薄尾分布不同，出现沉重尾巴的错误术语不仅概括了标准假设，而且还解决了决策不确定性行为。风险中的灵活决策规则被整合到代表总和部分锁定期间时间使用时间安排的离散连续选择模型中。最终，这项研究的研究成果为政策制定者提供了指导，以了解如何最好地实施社会疏远和隔离措施，以控制重大的流行病，然后如何最好地逐步淘汰这些措施。这项奖项反映了NSF的法定任务，并通过评估该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Willingness to delay charging of electric vehicles

电动汽车推迟充电的意愿

DOI：
发表时间：
2020
期刊：
25th Annual Conference of the European Association Environmental and Resource Economists
影响因子：
0
作者：
Daziano, R.A.
通讯作者：
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

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
影响因子：
0
作者：
Wu, Wangwei;Daziano, Ricardo A.
通讯作者：
Daziano, Ricardo A.

Social distancing and store choice in times of a pandemic

疫情期间的社交距离和商店选择