[论文解读] Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications

Jina Suh, Eric Horvitz+2 more|arXiv (Cornell University)|Aug 17, 2020

Health disparities and outcomes参考文献 48被引用 10

一句话总结

本研究提出一种计算框架，利用网络搜索日志追踪新冠疫情背景下人群在人类需求上的大规模变化，结合马斯洛需求层次理论与双重差分法以校正季节性影响。研究发现，生理与安全需求显著上升，而自我实现等高层次需求则明显下降，且更长的居家令显著影响了社交与情感需求。

ABSTRACT

Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology. Yet, pandemic-related policy decisions cannot be made on health information alone. Decisions need to consider the broader impacts on people and their needs. Quantifying human needs across the population is challenging as it requires high geo-temporal granularity, high coverage across the population, and appropriate adjustment for seasonal and other external effects. Here, we propose a computational methodology, building on Maslow's hierarchy of needs, that can capture a holistic view of relative changes in needs following the pandemic through a difference-in-differences approach that corrects for seasonality and volume variations. We apply this approach to characterize changes in human needs across physiological, socioeconomic, and psychological realms in the US, based on more than 35 billion search interactions spanning over 36,000 ZIP codes over a period of 14 months. The analyses reveal that the expression of basic human needs has increased exponentially while higher-level aspirations declined during the pandemic in comparison to the pre-pandemic period. In exploring the timing and variations in statewide policies, we find that the durations of shelter-in-place mandates have influenced social and emotional needs significantly. We demonstrate that potential barriers to addressing critical needs, such as support for unemployment and domestic violence, can be identified through web search interactions. Our approach and results suggest that population-scale monitoring of shifts in human needs can inform policies and recovery efforts for current and anticipated needs.

研究动机与目标

• 理解疫情对生物医学之外的社会、经济与心理社会影响。
• 开发一种可扩展、保护隐私的方法，用于量化人群层面人类需求的变化。
• 识别网络搜索行为中反映未满足需求的政策相关信号，例如失业与家庭暴力。
• 实现实时监测人类需求，以支持未来疫情应对与恢复规划。
• 考察不同地区与弱势群体在需求表达上的差异。

提出的方法

• 本研究应用马斯洛需求层次理论与马克斯-尼夫框架，将三十五亿条以上的搜索查询归类为五大类：生理、安全、爱与归属、认知与自我实现。
• 通过基于规则的标注系统，将搜索查询字符串与点击交互映射至七十九个需求子类别，准确率达97%。
• 采用双重差分法，通过比较不同居家令持续时间的州之间需求表达的变化，隔离疫情的影响。
• 该方法控制季节性波动与基线搜索量趋势，以确保对相对需求变化的稳健估计。
• 分析基于美国14个月（疫情前7个月，疫情中7个月）超过36,000个邮政编码的网络搜索日志。
• 通过与已知公共卫生趋势及轶事报告对比，开展外部验证。

实验结果

研究问题

• RQ1 在疫情期间，人类基本需求在生理、社会经济与心理领域中的表达发生了怎样的变化？
• RQ2 州级居家令时长与社交及情感需求变化之间的相关性有多大？
• RQ3 网络搜索数据能否检测到获取关键支持服务（如失业援助或家庭暴力资源）的潜在障碍？
• RQ4 需求表达的区域差异如何反映政策响应中的脆弱性？
• RQ5 基于搜索的信号在多大程度上可预测疫情相关中断带来的长期心理健康与经济影响？

主要发现

• 疫情期间，生理与安全需求呈指数级增长，而自我实现与认知发展等高层次需求显著下降。
• 居家令持续时间较长的州，其社交与情感需求的表达显著且持续上升。
• 尽管轶事报告称家庭暴力事件上升，但与家庭暴力相关的搜索查询却出现显著下降，提示求助可能存在障碍。
• 求职与教育学位相关查询持续低迷，表明长期经济与教育系统受到冲击。
• 该框架通过搜索模式异常成功识别出存在未满足需求的地区，如经济困难与心理健康问题。
• 该方法在将搜索查询分类至五大类需求时达到97%的准确率，验证了其在大规模行为分析中的可靠性。