RAPID: development of a local epidemiological population balance model informed by UAV and WVD data

RAPID：根据无人机和 WVD 数据开发当地流行病学人口平衡模型

• 批准号: 2040503
• 负责人: Norman Wagner
• 金额: $ 10万
• 依托单位: University of Delaware
• 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: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040503&HistoricalAwards=false
• 关键词: RAPID; development; local; epidemiological; population

Decision making and policy setting by universities and surrounding localities requires knowledge of how people move and interact in the environment. This RAPID project adapts new scientific approaches in population balance modeling to model human movement and interaction on a university campus and a surrounding town with the goal of providing new tools to help develop rational strategies for mitigation and eventual elimination of the novel corona virus, as well as future biological threats. Data for the model input will be obtained from high-definition video footage of public, outdoor areas including green spaces/parks, sidewalks/streets, and campus walkways/congregating spaces analyzed by artificial intelligence algorithms. Highly efficient tools that were originally developed to study complex fluids will enable determination key parameters needed for epidemiological models including effective transmission rates. Epidemiological modeling will be translated into a dashboard for use by policy makers as well as for public education about mitigation strategies. This RAPID project will provide a computational tool and example for use more broadly by communities and in additional and future, challenging public health issues.A multivariate population balance model applied to a college and local municipality will generate key parameters for agent-based epidemiological models. Multivariate balance modeling will be challenged with new data sets of local population density and motion for model parameter estimation using parallel tempering developed under prior and current NSF support. In addition to the usual distinctions of immune, susceptible, exposed, infected, and recovered classes, additional variables to consider include: age, especially relevant for University students, face-covering, inside and outside, and spatial-temporal population distributions afforded by real time updates of aerial (unmanned aerial vehicle) and ground (stationary camera augmented by wearable video devices) surveillance data. While it is common to include coarse-grained information afforded by transportation networks in large-scale epidemiology models, this project will explore opportunities afforded by social force models combined with epidemic population balance modeling. Advanced parallel tempering algorithms will be run on a GPU cluster to challenge the model with daily data streams to update parameters for epidemiological models and scenario projections. A project dashboard will be made available for policy decision making and public education. Broader impacts include computational tools that can be applied to a broad range of public health issues.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.

大学和周边地区的决策和政策制定需要了解人们如何在环境中移动和互动。 该 RAPID 项目采用人口平衡建模中的新科学方法来模拟大学校园和周边城镇的人类活动和互动，目标是提供新工具来帮助制定缓解和最终消除新型冠状病毒以及未来生物威胁的合理策略。模型输入的数据将从公共、室外区域的高清视频片段中获得，包括绿地/公园、人行道/街道以及校园走道/聚集空间，并通过人工智能算法进行分析。最初开发用于研究复杂流体的高效工具将能够确定流行病学模型所需的关键参数，包括有效传播率。流行病学模型将转化为仪表板，供政策制定者使用以及有关缓解策略的公众教育。 该 RAPID 项目将提供一个计算工具和示例，供社区更广泛地使用，并在未来解决公共卫生问题。应用于大学和地方政府的多元人口平衡模型将为基于主体的流行病学模型生成关键参数。多元平衡建模将面临当地人口密度和运动的新数据集的挑战，该数据集使用在先前和当前 NSF 支持下开发的并行回火来估计模型参数。 除了免疫类、易感类、暴露类、感染类和康复类的常见区别外，需要考虑的其他变量包括：年龄（尤其与大学生相关）、面部遮盖、内部和外部以及空中（无人机）和地面（可穿戴视频设备增强的固定摄像机）监控数据实时更新提供的时空人口分布。虽然大规模流行病学模型中通常会包含交通网络提供的粗粒度信息，但该项目将探索社会力量模型与流行病人口平衡模型相结合所提供的机会。 先进的并行调温算法将在 GPU 集群上运行，以每日数据流挑战模型，以更新流行病学模型和场景预测的参数。 将提供项目仪表板用于政策决策和公共教育。更广泛的影响包括可应用于广泛公共卫生问题的计算工具。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。