RAPID: Developing Advanced Modeling and Analysis Tools to track Human Movement Patterns and Coronavirus / Infectious Disease Spread Dynamics in Geographical Networks

RAPID：开发先进的建模和分析工具来跟踪地理网络中的人类运动模式和冠状病毒/传染病传播动态

• 批准号: 2026875
• 负责人: Hui Yang
• 金额: $ 20万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2020-04-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026875&HistoricalAwards=false
• 关键词: RAPID; Developing; Advanced; Modeling; Analysis

The broader impact of this RAPID proposal is to to inform policy associated with pandemic viruses, such as coronavirus disease 2019 (COVID-19), as they significantly impact the national health and economy. To safeguard society from viruses, each country/state/locality needs to dynamically adjust health policies, plan near-term health care capacity, and control population movement with little time latency. Accurate real-time prediction of virus spread is essential for making the health system respond in a fast and proactive manner to disease variations and disruption events (e.g., staffing and supply shortage). This project pursues fundamental research to develop simulation models of human movement and virus spread dynamics, prediction of real-time positions of infected population in the spato-geographic network, and development of decision support tools for the design of healthcare policies under disruptive events and processes. The proposed research is at the interface of engineering and public health to gain a better understanding of disease spread dynamics from both perspectives. Effective simulation analysis and prediction of virus positions in geographic regions will not only help optimize the design of healthcare policies to control the propagation of infectious diseases, but also help safeguard the US population and make health systems more resilient to disruptive events.This RAPID project will leverage data analytics and simulation models to gain a better understanding of virus spreading dynamics. The objective of this research project is to develop continuous flow simulation modeling and analysis of human movement/traffic and virus spread dynamics in spatial networks. Specifically, we will investigate the derivation of health policies and infectious disease control so that the healthcare system can respond expeditiously and effectively to disruptive events. The proposed project will study three sets of policy-relevant characteristics that are central to the understanding of the impact of public interventions on virus spread, namely regional infrastructure of health care delivery, regulatory measures to slow down the virus spread, and effectiveness of information transparency. As the dynamics of infectious diseases often change over time, simulation models and analytical algorithms from this project help support decision-making in real time. The proposed methodology is generally applicable to a wide range of infectious diseases.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.

这项快速提议的更广泛影响是为与大流行病有关的政策提供信息，例如2019年冠状病毒病（Covid-19），因为它们会对国家健康和经济产生重大影响。为了保护社会免受病毒的侵害，每个国家/州/地区都需要动态调整健康政策，计划近期的卫生保健能力以及控制人口运动，而随着时间的延迟而控制人口运动。准确的病毒传播实时预测对于使卫生系统以快速而主动的方式对疾病变化和破坏事件（例如人员配备和供应短缺）做出反应至关重要。该项目追求基础研究，以开发人类运动和病毒的模拟模型传播动态，预测Spato地理网络中受感染人群的实时位置，以及开发在破坏性事件和过程下设计医疗保健政策的决策支持工具。拟议的研究是在工程和公共卫生的界面上，从这两个角度可以更好地了解疾病传播动态。有效的仿真分析和对地理区域病毒位置的预测不仅将有助于优化医疗保健政策的设计，以控制传染性疾病的传播，而且还有助于维护美国人口，并使卫生系统更有弹性，使其更有弹性地破坏性事件。该快速项目将利用数据分析和模拟模型来更好地了解病毒性的动力学。该研究项目的目的是开发人类运动/流量和病毒在空间网络中传播动态的连续流仿真建模和分析。具体而言，我们将研究健康政策和传染病控制的推导，以便医疗保健系统可以迅速有效地对破坏性事件做出反应。拟议的项目将研究三组​​与政策相关的特征，这是对公共干预措施对病毒蔓延的影响的核心，即医疗保健提供的区域基础设施，监管措施，以减缓病毒蔓延以及信息透明度的有效性。随着传染病的动态经常会随着时间的流逝而变化，该项目的模拟模型和分析算法有助于实时支持决策。提出的方法通常适用于广泛的传染病。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Network Modeling and Analysis of COVID-19 Testing Strategies

COVID-19 测试策略的网络建模和分析

• DOI: 10.1109/embc46164.2021.9629754
• 发表时间: 2021
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Zhang, Siqi;Ventura, Marta J.;Yang, Hui
• 通讯作者: Yang, Hui

Spatial Modeling and Analysis of Human Traffic and Infectious Virus Spread in Community Networks

社区网络中人流量和传染性病毒传播的空间建模与分析

• DOI: 10.1109/embc46164.2021.9630798
• 发表时间: 2021
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Zhang, Siqi;Yang, Hui
• 通讯作者: Yang, Hui

Statistical Analysis of Spatial Network Characteristics in Relation to COVID-19 Transmission Risks in US Counties

美国各县与 COVID-19 传播风险相关的空间网络特征统计分析

• DOI: 10.1109/embc46164.2021.9629892
• 发表时间: 2021
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Zhang, Siqi;Yang, Sihan;Yang, Hui
• 通讯作者: Yang, Hui