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

全球大流行期间的不确定性，如新型冠状病毒病(新冠肺炎)，可能会产生恐惧和焦虑，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会进一步加剧这种情况，导致应急管理人员和迫切需要的人的关键疾病预防产品短缺。消费者的反应也可能根据人口统计数据和社区互动而有所不同。该项目旨在了解消费者恐慌性购买、感染病例报告以及该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。从根本上了解社区因素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出做出有效和及时的决策，防止大型和多样化的城市中心出现关键物资的意外短缺。此外，该项目开发的量化方法在工程学、计算机科学、社会科学和卫生科学之间架起了桥梁，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全面的视角。这一快速项目的主要重点是开发一个多网络框架，该框架捕捉管理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存关系。对消费者购买行为、社会经济社区特征等各种因素之间关系的基本了解，以及可获得健康信息的程度，能够评估诸如关键疾病预防用品短缺等潜在后果。从纽约选定社区的新冠肺炎体验中获得的数据和众包信息被用作验证研究的案例研究。对多方面的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物资，以减轻恐慌性购买的影响。识别基本的复杂和相互依赖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洞察力，以应对紧急情况下的恶性恐慌循环，从而促进城市弹性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.