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.

该项目的目的是创建一个软件基础架构，该基础架构将帮助科学家研究Covid-19的传播风险，并使用实时公共网络摄像头视频和基于位置的服务（LBS）数据在拥挤的位置分析未来的情节。这是由于观察到Covid-19群集经常出现在涉及高密度的人群的情况下。当前的策略表明了防止这种情况的粗略干预措施，例如取消活动，这会导致大量的经济和社会成本。对拥挤地点的人们的运动和互动模式进行更详细的缩放分析可以提出干预措施，例如人群管理程序的变化和建筑环境的设计，这些距离会产生社交距离而不会对人类活动和经济造成任何破坏。行人动力学领域提供了数学模型，可以生成此类详细的见解。但是，这些模型需要有关人类行为的数据，这些数据随上下文和文化而变化很大。该项目将利用新颖的数据流，例如公共网络摄像头和基于位置的服务，以告知行人动态模型。相关的数据，模型和软件将被提供，以使在该领域工作的其他研究人员受益，但要受到隐私限制。项目团队还将向决策者进行宣传，以便科学见解产生有助于公共卫生的可行政策。最终结果将是至关重要的科学见解，它可以对对Covid-19的大流行的反应产生变革性的影响，包括可能的第二波，因此它可以保护公共卫生，同时最大程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数据可以在数十米的规模上识别拥挤的位置，并通过分析那里的个人的远程运动来帮助筛查潜在的风险。全球视频流可以产生社会亲密关系和其他行为模式的细节细节，以进行准确的建模。另一方面，这些视频可能无法用于潜在的高风险位置，也无法直接回答“假设”问题。与正在建模的上下文相似的上下文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，将在目标位置模拟个人行人的轨迹，以估计社会亲密关系。感染传播模型将应用于这些轨迹，以产生感染扩散的估计值。这将导致一种新颖的方法，将潜水员的实时数据包括在行人动力学模型中，以便它们可以快速，准确地捕获新的和不断发展的情况下的人类运动模式。网络基础设施将自动发现Internet上的实时视频流，并分析它们以确定行人的密度，运动和社交距离。行人动力学模型将从基于当前力量的定义重新制定到使用行人密度和个体速度的定义，这两者都可以通过视频分析有效地衡量。 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 precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.