Collaborative:RAPID: Leveraging New Data Sources to Analyze the Risk of COVID-19 in Crowded Locations

协作：RAPID：利用新数据源分析拥挤场所中的 COVID-19 风险

• 批准号: 2027529
• 负责人: Matthew Scotch
• 金额: $ 4.97万
• 依托单位: Arizona 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: 2020-05-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027529&HistoricalAwards=false
• 关键词: Collaborative; RAPID; Leveraging; New; Data

The goal of this project is to create a software infrastructure that will help scientists investigate the risk of the spread of COVID-19 and analyze future epidemics in crowded locations using real-time public webcam videos and location based services (LBS) data. It is motivated by the observation that COVID-19 clusters often arise at sites involving high densities of people. Current strategies suggest coarse scale interventions to prevent this, such as cancellation of activities, which incur substantial economic and social costs. More detailed fine scaled analysis of the movement and interaction patterns of people at crowded locations can suggest interventions, such as changes to crowd management procedures and the design of built environments, that yield social distance without being as disruptive to human activities and the economy. The field of pedestrian dynamics provides mathematical models that can generate such detailed insight. However, these models need data on human behavior, which varies significantly with context and culture. This project will leverage novel data streams, such as public webcams and location based services, to inform the pedestrian dynamics model. Relevant data, models, and software will be made available to benefit other researchers working in this domain, subject to privacy restrictions. The project team will also perform outreach to decision makers so that the scientific insights yield actionable policies contributing to public health. The net result will be critical scientific insight that can generate a transformative impact on the response to the COVID-19 pandemic, including a possible second wave, so that it protects public health while minimizing adverse effects from the interventions.We will accomplish the above work through the following methods and innovations. LBS data can identify crowded locations at a scale of tens of meters and help screen for potential risk by analyzing the long range movement of individuals there. Worldwide video streams can yield finer-grained details of social closeness and other behavioral patterns desirable for accurate modeling. On the other hand, the videos may not be available for potentially high risk locations, nor can they directly answer “what-if” questions. Videos from contexts similar to the one being modeled will be used to calibrate pedestrian dynamics model parameters, such as walking speeds. Then the trajectories of individual pedestrians will be simulated in the target locations to estimate social closeness. An infection transmission model will be applied to these trajectories to yield estimates of infection spread. This will result in a novel methodology to include diverse real time data into pedestrian dynamics models so that they can quickly and accurately capture human movement patterns in new and evolving situations. The cyberinfrastructure will automatically discover real-time video streams on the Internet and analyze them to determine the pedestrian density, movements, and social distances. The pedestrian dynamics model will be reformulated from the current force-based definition to one that uses pedestrian density and individual speed, both of which can be measured effectively through video analysis. The revised model will be used to produce scientific insight to inform policies, such as steps to mitigate localized outbreaks of COVID-19 and for the systematic reopening, potential re-closing, and permanent changes to economic and social activities.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.

该项目的目标是创建一个软件基础设施，帮助科学家调查新冠肺炎传播的风险，并使用实时公共网络摄像头视频和基于位置的服务(LBS)数据分析未来在人口稠密地点的疫情。其动机是观察到新冠肺炎集群通常出现在人口密度较高的地点。目前的战略建议采取粗略的干预措施来防止这种情况，例如取消造成巨大经济和社会成本的活动。对人群在拥挤地点的运动和互动模式进行更详细、更精细的分析，可以提出干预措施，例如改变人群管理程序和建筑环境的设计，从而在不破坏人类活动和经济的情况下产生社会距离。行人动力学领域提供了可以产生如此详细洞察力的数学模型。然而，这些模型需要关于人类行为的数据，这在背景和文化上有很大的差异。该项目将利用新的数据流，如公共网络摄像头和基于位置的服务，为行人动力学模型提供信息。相关数据、模型和软件将提供给在该领域工作的其他研究人员，但受隐私限制。项目组还将与决策者进行接触，以使科学见解产生有助于公共卫生的可行政策。最终结果将是关键的科学见解，能够对新冠肺炎大流行的应对产生革命性影响，包括可能的第二波，从而保护公众健康，同时将干预的不利影响降至最低。我们将通过以下方法和创新完成上述工作。LBS数据可以在几十米的范围内识别拥挤的位置，并通过分析那里的个人的远程移动来帮助筛选潜在的风险。在全球范围内，视频流可以产生更细粒度的社交亲密细节和其他准确建模所需的行为模式。另一方面，这些视频可能不适用于潜在的高危地点，也不能直接回答“假设”问题。类似于正在建模的场景中的视频将被用于校准行人动力学模型参数，如行走速度。然后，在目标位置模拟单个行人的轨迹，以估计社会亲密度。感染传播模型将应用于这些轨迹，以得出感染传播的估计。这将产生一种新的方法，将各种实时数据包含到行人动力学模型中，以便它们能够在新的和不断变化的情况下快速而准确地捕捉人类的运动模式。网络基础设施将自动发现互联网上的实时视频流，并对其进行分析，以确定行人密度、移动和社交距离。行人动力学模型将从目前基于力的定义重新制定为使用行人密度和个人速度的定义，这两种定义都可以通过视频分析进行有效测量。修订后的模型将用于产生科学见解，为政策提供参考，例如缓解新冠肺炎局部爆发的措施，以及对经济和社会活动的系统性重新开放、潜在重新关闭和永久性变化。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。