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

在新型冠状病毒病（COVID-19）等全球大流行期间，不确定性会产生恐惧和焦虑，导致恐慌性购买和过度反应的消费者行为。来自多种来源的信息可能使情况进一步恶化，导致应急管理人员和急需人员的关键疾病预防产品短缺。消费者的反应也可能因人口统计和社区互动而有所不同。该项目旨在了解在一个人口密集的大型病毒中心城市，消费者恐慌购买、感染病例报告和当地人口统计数据之间的关系。对社区因素的基本了解和紧急情况下消费者行为报告的作用，将有助于对资源规划和支付作出有效和及时的决定，防止在大型和多样化的城市中心出现关键供应品的意外短缺。此外，本项目开发的定量方法将工程、计算机科学、社会和保健科学等学科结合起来，创造了一种新的跨学科范式，为城市中心的应急准备和灾害管理提供了一个整体视角。快速发展项目的主要重点是制定一个多网络框架，以掌握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。对各种因素（如消费者购买行为、社会经济社区特征和可获得卫生信息的程度）之间关系的基本了解，使评估诸如关键疾病预防用品短缺等潜在结果成为可能。从选定的纽约市社区的COVID-19经验中获得的数据和众包信息被用作验证研究的案例研究。对消费者行为的多方面的准确理解，使决策分析能够有效地规划和有针对性地支付关键物资，以减轻恐慌性购买的影响。识别潜在的复杂和相互依存的网络结构为设计公平有效的资源规划和分配战略提供了见解，以解决紧急情况下的恶性恐慌循环，从而提高城市的复原力。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。

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.