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: 2027518
• 负责人: Sirish Namilae
• 金额: $ 5万
• 依托单位: Embry-Riddle Aeronautical 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 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027518&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）数据分析拥挤地点的未来流行病。其动机是观察到COVID-19集群通常出现在涉及高人口密度的地点。目前的战略建议采取大规模的干预措施来防止这种情况，例如取消活动，这会产生巨大的经济和社会成本。对人群在拥挤场所的运动和互动模式进行更详细的精细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，从而产生社会距离，而不会对人类活动和经济造成破坏。行人动力学领域提供了可以生成这种详细见解的数学模型。然而，这些模型需要关于人类行为的数据，而这些数据会因环境和文化而显着变化。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，为行人动态模型提供信息。相关的数据、模型和软件将被提供给在该领域工作的其他研究人员，但受隐私限制。项目团队还将与决策者进行外联，以便科学见解产生有助于公共卫生的可操作政策。最终结果将是关键的科学见解，可以对应对COVID-19大流行产生变革性影响，包括可能的第二波，以便保护公共卫生，同时最大限度地减少干预措施的不利影响。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位置，并通过分析那里的个人的长距离移动来帮助筛选潜在的风险。世界范围内的视频流可以产生更细粒度的社会亲密度和其他行为模式的细节，这些都是精确建模所需要的。另一方面，视频可能不适用于潜在的高风险地点，也不能直接回答“假设”问题。来自类似于被建模的背景的视频将用于校准行人动力学模型参数，例如步行速度。然后在目标位置模拟单个行人的轨迹，以估计社交亲密度。将对这些轨迹应用感染传播模型，以估计感染传播。这将导致一种新的方法，包括不同的真实的时间数据到行人动态模型，使他们能够快速，准确地捕捉人类运动模式在新的和不断变化的情况。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、运动和社交距离。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，这两者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，可能重新关闭以及经济和社会活动的永久性变化。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。