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RAPID提案的更广泛影响是为与2019冠状病毒病（COVID-19）等大流行病毒相关的政策提供信息，因为它们严重影响国家卫生和经济。为了保护社会免受病毒的侵害，每个国家/州/地区需要动态调整卫生政策，规划近期卫生保健能力，并在短时间内控制人口流动。准确的病毒传播实时预测对于使卫生系统以快速和主动的方式应对疾病变化和中断事件（例如人员配备和供应短缺）至关重要。本项目进行基础研究，开发人类运动和病毒传播动态模拟模型，预测受感染人群在空间地理网络中的实时位置，开发决策支持工具，用于在破坏性事件和过程中设计医疗保健政策。拟议的研究是在工程和公共卫生的界面上，从这两个角度更好地了解疾病传播动态。有效的模拟分析和预测病毒在地理区域的位置，不仅有助于优化医疗保健政策的设计，控制传染病的传播，而且有助于保护美国人口，使卫生系统更有弹性地应对破坏性事件。该RAPID项目将利用数据分析和模拟模型来更好地了解病毒传播动态。本研究项目的目标是发展空间网络中人类运动/交通和病毒传播动态的连续流模拟建模和分析。具体而言，我们将调查卫生政策和传染病控制的衍生，以便医疗保健系统能够迅速有效地应对破坏性事件。拟议的项目将研究对理解公共干预措施对病毒传播的影响至关重要的三组与政策相关的特征，即卫生保健服务的区域基础设施、减缓病毒传播的监管措施和信息透明度的有效性。由于传染病的动态经常随着时间的推移而变化，该项目的模拟模型和分析算法有助于实时支持决策。所建议的方法一般适用于范围广泛的传染病。该奖项反映了美国国家科学基金会的法定使命，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。

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