RAPID: Visual Analytics Approach to Real-Time Tracking of COVID-19

与感染率、高危人群、流动性和通勤动态相关的COVID-19数据正迅速从多个来源获得。然而，缺乏与数据驱动工具相结合的交互式视觉决策环境，以帮助公共卫生和社区领导人了解物理距离和其他缓解策略等各种因素如何影响疾病传播，帮助拉平曲线，实现经济复苏，同时最大限度地减少重新开放带来的公共卫生风险。该项目将开发用于跟踪COVID-19的可视化分析工具，并提出有效遏制疫情的平衡干预策略。 拟议的可视化分析系统集成了异构数据集，并使相关的分析模型和数据工程的应用，在复杂和不断变化的危机决策支持。其目标包括：（1）根据发病率、人口脆弱性、流动模式和减灾活动开发恢复预测模型;（2）了解公众情绪和风险认知的社交媒体工具;（3）通过数据工程和可视化分析原则进行模型改进诊断的可视化界面。决策框架将提供新的见解，缩小数据和决策之间的差距，并由广泛的伙伴关系合作的投入驱动，以提高可靠性和可用性。数据驱动的工具将有助于提高决策者从多个变量对疾病动态的理解。流行病学家可以利用这些见解，根据干预因素及其对人群行为的影响创建更高保真的模型。地方当局还可以利用这些模型做出拯救生命的决定，同时尽量减少对经济的影响。该项目将促成新的公共和私人合作伙伴关系，包括新奥尔良市和NSF视觉和决策信息中心的行业咨询委员会。该项目将通过开发分析产品的实践研究经验使研究生和本科生受益。项目成果将包括分析仪表板、源代码、模型和从多个来源收集的数据。仪表板、项目描述、数据源列表及其元数据沿着将在www.vastream.net上公布，为期两年。在项目期间，COVID-19组件的门户网站面向公众的部分将在中断中断的情况下转移到亚马逊云。 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。

COVID-19 data, related to infection rates, at-risk populations, mobility, and commute dynamics are rapidly becoming available from several sources. However, there is a lack of interactive visual decision-making environments integrated with data-driven tools to help public health and community leaders understand how various factors such as physical distancing and other mitigation strategies, impact the spread of disease, help flatten the curve, enabling economic recovery while minimizing public health risk due to reopening. This project will develop visual analytic tools for tracking COVID-19 and propose balanced intervention strategies for effective containment of the outbreak. The proposed visual analytics system integrates heterogeneous datasets and enables the application of relevant analytical models and data-engineering for decision support in a complex and evolving crisis. The objectives include the development of (1) forecasting models for recovery based on incidence, population vulnerabilities, mobility patterns, and mitigation activities, (2) social-media tools to understand public sentiment and risk perceptions, (3) visual interface for model-refinement & diagnosis through data engineering and visual analytics principles. The decision-making framework will offer new insights, close the gap between data and decisions, and is driven-by inputs from extensive partnerships & collaborations to improve reliability and usability. The data-driven tools will help improve decision makersí understanding of disease dynamics from multiple variables. Epidemiologists could potentially leverage these insights to create higher-fidelity models based on interventional factors and their effect on population behaviors. Local authorities could also utilize the models to make life-saving decisions while minimizing impact to the economy. The project will enable new public and private partnerships including the City of New Orleans, and Industry Advisory Board of NSF Center for Visual and Decision Informatics. The project will benefit graduate and undergraduate students through hands-on research experience with the development of analytical products. The project outcomes will include analytics dashboards, source code, models, and data collected from multiple sources. The dashboards, project descriptions, and a list of data sources along with their metadata will be made publicly available on www.vastream.net for a period of two years. The public facing portion of the portal for COVID-19 component will be moved to Amazon cloud in event of disruptions from outages, for the duration of the project. A new public repository will be created on GitHub, and the source code and publicly available datasets will be made available on this project repository.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.