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

COVID-19数据的初步统计分析显示，非裔美国人比美国其他族裔群体更受COVID-19影响。美国疾病控制和预防中心（CDC）最近的数据证实，黑人人口占美国病毒病例的30%，尽管它仅占美国人口的13%。在COVID-19的震中纽约市，数据还显示，黑人人口占COVID-19死亡人数的28%。VAPOC（可视化，分析和预测COVID-19）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。似乎是多种因素的结合导致了非洲裔美国人对COVID-19的易感性。这就提出了一个模式识别和知识发现的问题。据推测，先前存在的疾病、就业类型和获得医疗保健的机会等因素对2019冠状病毒病大流行期间非洲裔美国人的死亡率较高有重大影响。对非裔美国人社区的COVID-19进行可视化、分析和预测是必要的：1）社区充分了解减轻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。该项目是哥伦比亚特区大学和鲍伊州立大学的合作项目。为了实现研究目标，该项目的三个研究目标是：1）设计，开发和评估COVID-19模型，以确定对冠状病毒的脆弱性; 2）开发可视化和交互工具，以在沉浸式和非沉浸式环境中分析COVID-19患者的数据，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;以及3）设计、开发和评估深度学习模型，以预测COVID-19对出院患者的损害程度。VAPOC将神经网络预测与以人为本的态势感知和数据分析相结合，为打击和缓解新型冠状病毒在黑人社区的传播提供准确，及时和基于科学的策略。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的研究，数据分析和数据可视化，以提供可用于通知资源分配和机构政策的信息，以减少COVID的差异-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进行评估，被认为值得支持的搜索.

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.