RAPID: Effective Resource Planning and Disbursement during the COVID-19 Pandemic

在新型冠状病毒病（COVID-19）等全球流行病期间，不受影响会产生恐惧和焦虑，导致恐慌性购买和过度反应的消费者行为。来自众多来源的信息可能会进一步加剧这种情况，导致紧急情况管理人员和急需者缺乏关键的疾病预防产品。消费者的反应也可能因人口统计和社区互动而异。该项目旨在了解消费者恐慌性购买，感染病例报告和病毒大而人口稠密的城市中心的当地人口统计数据之间的关系。对社区因素和紧急情况下消费者行为报告的作用的基本了解将有助于有效和及时地做出资源规划和支出决策，防止大型和多样化城市中心的关键供应意外短缺。此外，在这个项目中开发的定量方法桥接工程，计算机科学，社会和健康科学的学科，创造了一个新的跨学科的范式，提供了一个整体的观点对应急准备和城市中心的灾害管理。该RAPID项目的主要重点是开发一个多网络框架，该框架捕捉了控制城市人口中信息传播、恐慌传播和疾病传播的网络之间的联系和相互依赖关系。对消费者购买行为、社会经济社区特征和可用健康信息程度等各种因素之间关系的基本了解，有助于评估潜在结果，如关键疾病预防用品短缺。来自纽约市选定社区的COVID-19经验的数据和众包信息被用作验证研究的案例研究。对多方面消费者行为的准确理解使决策分析能够有效地规划和有针对性地支付关键供应品，以减轻恐慌性购买的影响。通过对潜在的复杂和相互依赖的网络结构的识别，可以深入了解如何设计公平有效的资源规划和分配战略，以应对紧急情况下的恶性恐慌循环，从而促进城市的复原力。该奖项反映了NSF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。

Uncertainties during global pandemics, such as the novel Corona virus disease (COVID-19), can generate fear and anxiety, resulting in panic-buying and overreactive consumer behavior. Information from a multitude of sources may further exacerbate the situation, leading to shortages of critical disease prevention products for emergency managers and those in dire need. The consumer response may also vary based on population demographics and community interactions. This project aims to understand the relationships between consumer panic-buying, reports on infected cases, and local population demographics in a large and densely populated urban epicenter of the virus. Fundamental understanding of community factors and the role of reports on consumer behavior in emergencies will enable effective and timely decisions on resource planning and disbursement, preventing unexpected shortages of critical supplies in large and diverse urban centers. In addition, the quantitative methodologies developed in this project bridge the disciplines of engineering, computer science, social and health science, creating a new interdisciplinary paradigm that provides a holistic view towards emergency preparedness and disaster management in urban centers. The main focus of this RAPID project is to develop a multi-network framework that captures the linkages and inter-dependencies between networks that govern information spreading, panic spreading, and disease spreading in urban populations. Fundamental understanding of the relationships between various factors such as consumer buying behavior, socio-economic community characteristics, and the extent of available health information enables the assessment of potential outcomes such as shortages of critical disease prevention supplies. Data and crowdsourced information from the COVID-19 experience of selected NYC neighborhoods is used as a case study for validation studies. An accurate understanding of the multi-faceted consumer behavior enables decision analytics for effective planning and targeted disbursement of critical supplies for mitigating the effects of panic-buying. The identification of underlying complex and interdependent network structures provides insights into the design of equitable and effective strategies for resource planning and allocation to tackle the vicious panic cycle in emergencies, thus promoting urban resilience.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.