RAPID: Collaborative Research: VAPOC: Visualization, Analysis and Prediction of COVID-19

RAPID：协作研究：VAPOC：COVID-19 的可视化、分析和预测

• 批准号: 2032344
• 负责人: Sharad Sharma
• 金额: $ 4万
• 依托单位: Bowie State 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: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032344&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; VAPOC; Visualization

Preliminary statistical analysis of COVID-19 data shows that African Americans are more affected by COVID-19 than other ethnic groups in the USA. Recent data from the Centers for Disease Control and Prevention (CDC) confirms that the black population accounted for 30% of cases of the virus in the United States, although it is only approximately 13% of the US population. In New York city, an epicenter of COVID-19, data also show that the black population represents 28% of deaths due to COVID-19. The goal of the VAPOC (Visualization, Analysis and Prediction of COVID-19) project is to find out reasons as to why the black community is disproportionately impacted during the coronavirus pandemic. It seems a combination of factors is responsible for African Americans’ susceptibility to COVID-19. This poses a pattern recognition as well as knowledge discovery problem. It is hypothesized that pre-existing conditions, type of employment, and access to healthcare among other factors have significant influences in the higher death rate of African Americans during the COVID-19 pandemic. The visualization, analysis, and prediction of COVID-19 in the African American community is necessary for: 1) the community to be well informed about measures to ameliorate the impact of coronavirus and to reduce its spread, and 2) a proper understanding of what factors medical professionals should prioritize when performing health assessments and diagnostic tests for COVID-19 patients. VAPOC will also help decision-makers to improve mitigation strategies. This project is a collaborative effort between the University of the District of Columbia and Bowie State University.To accomplish the research goal, the three research objectives of this project are: 1) to design, develop and evaluate a COVID-19 model to determine vulnerability to coronavirus; 2) to develop a visualization and interaction tool to analyze COVID-19 patients’ data in an immersive and non-immersive environment, and evaluate how graphical objects (such as data-shapes) developed in accordance with the user’s requirements can enhance situational awareness; and 3) to design, develop and evaluate a deep learning model to predict the extent of COVID-19 damage to discharged patients. VAPOC combines neural network predictions with human-centric situational awareness and data analytics to provide accurate, timely and scientifically-based strategy for combating and mitigating the spread of the novel coronavirus in the black community. Ultimately, understanding how COVID-19 affects the black community will also provide criteria for mitigating the spread of future outbreaks. Furthermore, the project will leverage research in deep learning, data analytics and data visualization to provide information that could be used to inform the allocation of resources and institutional policies to reduce the disparity of COVID-19 deaths in the African American community.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.

对COVID-19数据的初步统计分析表明，非洲裔美国人比美国其他族裔受COVID-19影响更大。美国疾病控制与预防中心（CDC）的最新数据证实，尽管黑人仅占美国人口的13%左右，但黑人占美国病毒病例的30%。在COVID-19的中心纽约市，数据还显示，黑人占COVID-19死亡人数的28%。“COVID-19可视化、分析和预测”（VAPOC）项目的目标是找出黑人社区在冠状病毒大流行期间受到不成比例影响的原因。非洲裔美国人对COVID-19的易感性似乎是多种因素共同作用的结果。这就提出了一个模式识别和知识发现问题。据推测，在COVID-19大流行期间，已有的疾病、就业类型和获得医疗保健等因素对非裔美国人的高死亡率有重大影响。非洲裔美国人社区COVID-19的可视化、分析和预测对于以下方面是必要的：1)社区充分了解改善冠状病毒影响和减少其传播的措施；2)正确理解医疗专业人员在对COVID-19患者进行健康评估和诊断测试时应优先考虑哪些因素。VAPOC还将帮助决策者改进缓解战略。这个项目是哥伦比亚特区大学和鲍伊州立大学的合作成果。为了实现研究目标，本项目的三个研究目标是：1)设计、开发和评估COVID-19模型，以确定冠状病毒的脆弱性；2)开发可视化交互工具，在沉浸式和非沉浸式环境下分析COVID-19患者数据，并评估根据用户需求开发的图形对象（如数据形状）如何增强态势感知；3)设计、开发和评估深度学习模型，预测COVID-19对出院患者的损害程度。VAPOC将神经网络预测与以人为中心的态势感知和数据分析相结合，为抗击和减轻新型冠状病毒在黑人社区的传播提供准确、及时和科学的策略。最终，了解COVID-19如何影响黑人社区也将为减轻未来疫情的传播提供标准。此外，该项目将利用深度学习、数据分析和数据可视化方面的研究，提供可用于为资源分配和机构政策提供信息的信息，以缩小非洲裔美国人社区COVID-19死亡人数的差距。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。

项目成果

Situational awareness of COVID pandemic data using virtual reality

使用虚拟现实对新冠肺炎大流行数据进行态势感知

• DOI: 10.2352/issn.2470-1173.2021.13.ervr-177
• 发表时间: 2021
• 期刊: Electronic Imaging
• 作者: Sharma, Sharad;Bodempudi, Sri Teja
• 通讯作者: Bodempudi, Sri Teja

Data Visualization Tool for Covid-19 and Crime Data

Covid-19 和犯罪数据的数据可视化工具

• 发表时间: 2021
• 期刊: (CSCI'21
• 作者: Sean Walker;Sharad Sharma
• 通讯作者: Sharad Sharma

Improving Emergency Response Training and Decision Making Using a Collaborative Virtual Reality Environment for Building Evacuation

使用协作虚拟现实环境进行建筑物疏散改进应急响应培训和决策

• DOI: 10.1007/978-3-030-59990-4_17
• 发表时间: 2020
• 作者: Sharma, Sharad

Real-Time Data Analytics of COVID Pandemic Using Virtual Reality

使用虚拟现实对新冠疫情进行实时数据分析

• DOI: 10.1007/978-3-030-77599-5_9
• 发表时间: 2021
• 期刊: Lecture notes in computer science
• 作者: Sharma, S;Bodempudi, S.T;Reehl, A
• 通讯作者: Reehl, A

The Effect of COVID-19 on Various Racial Demographics in the United States

COVID-19 对美国各种族人口统计的影响

• 发表时间: 2020
• 期刊: 2020 International Conference on Computational Science and Computational Intelligence (CSCI
• 作者: Rayan, Trisha;Brown, Adrian;Carillo, Andrei;Sharma, Sharad
• 通讯作者: Sharma, Sharad