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，目前缺乏与数据驱动工具相结合的交互式可视化决策环境，以帮助公共卫生和社区领导人了解各种因素（如保持身体距离和其他缓解战略）如何影响疾病传播，帮助使曲线变平，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，并为有效遏制疫情提出平衡的干预策略。提出的可视化分析系统集成了异构数据集，并使相关分析模型和数据工程的应用能够在复杂和不断发展的危机中提供决策支持。目标包括开发(1)基于发生率、人口脆弱性、流动模式和减灾活动的恢复预测模型；(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和诊断的可视化界面。该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和协作的投入推动，以提高可靠性和可用性。数据驱动的工具将有助于改进决策makersí从多个变量了解疾病动态。流行病学家可能会利用这些见解，基于干预因素及其对人口行为的影响，创建更高保真度的模型。地方当局还可以利用这些模型做出挽救生命的决策，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视觉和决策信息学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。项目结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、项目描述和数据源列表及其元数据将在www.vastream.net上公开，为期两年。COVID-19组件门户的面向公众部分将在项目期间因中断而中断时转移到亚马逊云。一个新的公共存储库将在GitHub上创建，源代码和公开可用的数据集将在这个项目存储库上提供。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。

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.