Collaborative:Elements:RUI:Cyberinfrastructure for Pedestrian Dynamics-Based Analysis of Infection Propagation Through Air Travel

协作：元素：RUI：基于行人动力学的航空旅行感染传播分析的网络基础设施

基本信息

批准号：
1931511
负责人：
Sikha Bagui
金额：
$ 35万
依托单位：
University of West Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-11-01 至 2024-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931511&HistoricalAwards=false
关键词：
Collaborative Elements RUI Cyberinfrastructure Pedestrian

项目摘要

When people congregate - for example, at entertainment events, in crowds, and airplanes - they come into close contact with each other and can spread infectious diseases. The Disney World measles outbreak in 2016 is a prominent example. Air travel, in particular, is a leading factor in the spread of infections, and there have been several outbreaks of serious diseases that spread during air travel, such as SARS, H1N1 influenza, and tuberculosis. Public health policies and procedures for crowd management, boarding airplanes, etc. can help in mitigating the spread of disease, if these policies are science-based. The spread of directly transmitted diseases is governed by the movement patterns of people because the movement can bring an infected person close to others. The science of "pedestrian dynamics" provides mathematical models that can accurately simulate the movement of individuals in a crowd. These models allow scientists to understand how different policies, such as boarding procedures on planes, can prevent, or make worse, the transmission of infections. This project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. The project team is working closely with decision makers in airports, public health agencies, and the airline industry. This collaboration will lead to practical applications of this science that will improve public health. This project and the software will educate a wide range of scientists as well as students, in particular, students from under-represented groups, as well as professionals working in the public health fields.This project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. Development of the proposed software will involve several innovations. It will include a novel phylogeography model that links fine-scale human movement data with virus genetic information to more accurately model geographic diffusion of viruses. New models for pedestrian movement will enable modeling of complex human movement patterns. A recommendation system for the choice of pedestrian dynamics models and a domain specific language for the input of policies and human behaviors will enhance usability by researchers in diverse fields. Community building initiatives will catalyze inter-disciplinary research to ensures the long-term sustainability of the project through a critical mass of contributors and users.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.

当人们聚集时——例如在娱乐活动、人群和飞机上——他们会相互密切接触，并可能传播传染病。 2016 年迪士尼乐园爆发的麻疹疫情就是一个突出的例子。尤其是航空旅行，是感染传播的主要因素，已经爆发过多起通过航空旅行传播的严重疾病的疫情，例如SARS、H1N1流感和肺结核。如果人群管理、登机等公共卫生政策和程序有科学依据，则可以帮助减轻疾病的传播。直接传播疾病的传播受人们的移动模式控制，因为移动可以使感染者与其他人近距离接触。 “行人动力学”科学提供了可以准确模拟人群中个体运动的数学模型。这些模型使科学家能够了解不同的政策（例如飞机登机程序）如何预防或加剧感染的传播。该项目旨在开发一种新颖的软件，该软件将提供各种行人动力学模型、感染传播模型以及数据，以便科学家能够分析不同机制对拥挤地区直接传播疾病传播的影响。该项目最初的重点是航空旅行。然而，该软件可以扩展到运动分析和流行病学方面更广泛的应用范围，例如主题公园和体育场馆。该项目团队正在与机场、公共卫生机构和航空业的决策者密切合作。此次合作将推动这门科学的实际应用，从而改善公众健康。该项目和该软件将教育广泛的科学家和学生，特别是来自代表性不足群体的学生以及公共卫生领域的专业人士。该项目旨在开发一种新颖的软件，该软件将提供各种行人动力学模型、感染传播模型以及数据，以便科学家能够分析不同机制对拥挤地区直接传播疾病传播的影响。该项目最初的重点是航空旅行。然而，该软件可以扩展到运动分析和流行病学方面更广泛的应用范围，例如主题公园和体育场馆。拟议软件的开发将涉及多项创新。它将包括一种新颖的系统发育地理学模型，该模型将精细尺度的人类运动数据与病毒遗传信息联系起来，以更准确地模拟病毒的地理扩散。行人运动的新模型将使复杂的人类运动模式建模成为可能。用于选择行人动力学模型的推荐系统以及用于输入政策和人类行为的特定领域语言将增强不同领域研究人员的可用性。社区建设举措将促进跨学科研究，通过足够数量的贡献者和用户确保项目的长期可持续性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics

使用行人动力学进行高效计算感染风险分析的参数扫描评估

DOI：
发表时间：
2023-03
期刊：
Proceedings IEEE Aerospace Conference
影响因子：
0
作者：
Islam, Tasvirul;Ashok Srinivasan;Sirish Namilae
通讯作者：
Sirish Namilae

Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies

美国的人群聚类数据用于分析人类对 COVID-19 事件和政策的反应

DOI：
发表时间：
2023-01
期刊：
Scientific data
影响因子：
9.8
作者：
Swaminathan, B.;J. Kang;K. Vaidya;A. Srinivasan;P. Kumar;S. Byna;D. Barbarash
通讯作者：
D. Barbarash

Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics

使用行人动力学进行高效计算感染风险分析的参数扫描评估

DOI：
10.1109/aero55745.2023.10115639
发表时间：
2023-03-04
期刊：
2023 IEEE Aerospace Conference
影响因子：
0
作者：
T. Islam;A. Srinivasan;S. Namilae
通讯作者：
S. Namilae

From bad to worse: airline boarding changes in response to COVID-19

情况每况愈下：针对新冠肺炎 (COVID-19) 疫情，航空公司登机有所变化

DOI：
10.1098/rsos.201019
发表时间：
2020-06-04
期刊：
Royal Society Open Science
影响因子：
3.5
作者：
T. Islam;M. S. Lahijani;A. Srinivasan;S. Namilae;A. Mubayi;M. S. U. O. W. Florida;Florida State University;Embry;Arizona State University
通讯作者：
Arizona State University

Should N95 Respirators be Recommended for the General Public

是否应该向公众推荐 N95 呼吸器