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 COVID-19 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) 数据分析拥挤场所的未来流行病。观察到 COVID-19 聚集性事件经常发生在人员密度较高的地点，目前的策略建议采取粗略的干预措施来防止这种情况，例如取消活动，这会产生大量的经济和社会成本，并进行更详细的精细分析。人们的互动模式在拥挤的地方可以建议采取干预措施，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交距离。行人动力学领域提供了可以生成如此详细的见解的数学模型。然而，这些模型需要有关人类行为的数据，这些数据因环境和文化而异，该项目将利用公共网络摄像头和基于位置的服务等新颖的数据流来为行人动态模型提供信息。将有利于该领域、主题工作的其他研究人员项目团队还将向决策者进行推广，以便科学见解产生有助于公共卫生的可行政策，最终结果将是对应对 COVID-19 大流行产生变革性影响。包括可能出现的第二波疫情，这样既可以保护公众健康，又可以最大程度地减少干预措施带来的不利影响。我们将通过以下方法和创新来完成上述工作，LBS数据可以识别数十米范围内的拥挤地点并帮助筛查。通过分析个人的长距离移动来发现潜在风险全球视频流可以产生准确建模所需的更精细的社交亲密程度和其他行为模式，但这些视频可能无法用于潜在的高风险地点，也不能直接回答“假设”问题。来自与正在建模的环境相似的视频将用于校准行人动态模型参数，例如步行速度，然后将在目标位置模拟单个行人的轨迹，以估计感染传播模型。应用这将产生一种新颖的方法，将各种实时数据纳入行人动态模型中，以便它们能够快速准确地捕获新的和不断变化的情况下的人类运动模式。行人动态模型将从当前基于力的定义重新制定为使用行人密度和个人速度的模型，这两者都可以通过互联网上的时间视频流进行分析。通过视频分析进行有效测量。该模型将用于产生科学见解，为政策提供信息，例如缓解 COVID-19 局部爆发的措施以及系统性重新开放、可能的重新关闭以及对经济和社会活动的永久性改变。该奖项反映了 NSF 的法定使命和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，该项目被认为值得支持。