Network-based Modeling of Infectious Disease Epidemics in a Mobile Population: Strengthening Preparedness and Containment

基于网络的流动人口传染病流行模型：加强防备和遏制

• 批准号: 1561134
• 负责人: Maurizio Porfiri
• 金额: $ 37.5万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561134&HistoricalAwards=false
• 关键词: Network; based; Modeling; Infectious; Disease

Mathematical models of infectious disease spread are potent tools for the management of dangerous outbreaks. These models can form a basis for planning and implementing vaccination strategies, evaluating the risks and benefits of travel bans, and improving the effectiveness of prophylaxis campaigns. However traditional modeling approaches do not fully capture the national and international mobility characteristic of modern society, where contacts do not remain geographically confined to the area of the initial outbreak, and an infection may jump thousands of miles in a single day. This project will advance fundamental understanding of dynamical systems evolving on reconfigurable networks, in which the subsystems and the network connections change on comparable time-scales. The resulting mathematical framework will enable a new class of predictive models of infectious disease spread. These models will aid in safeguarding uninfected populations and in mitigating impact on afflicted nations, even when, as in the case of Ebola Virus Disease, no therapeutic protocol is available. More broadly, the underlying theoretical advances are expected to transform the analysis, design, and control of dynamical systems on rapidly reconfiguring networks. Complementing the research component of this project is outreach to promote the education of underprivileged students and to serve economically-disadvantaged communities.This research program seeks to advance the field of dynamical systems and complex networks toward tractable mathematical models of infectious disease epidemics. Specifically, this project will establish a theoretical framework for the study of the concurrent evolution of the dynamics of infectious diseases and the formation of the network of contacts through which they spread. The framework will be based on the notion of activity-driven networks, which can be effectively utilized to model contact processes that evolve over time-varying networks across a range of time-scales. This modeling paradigm contrasts that of traditional connectivity-driven networks, where links between nodes have a long life span, resulting in the separation between the time-scales of the dynamics of the network connections and the process evolution. The research team will seek to understand the effect of non-ideal containment procedures on the spread of infectious disease through the systematic analysis of global and local network features; devise strategies for community detection in time-varying networks, toward identifying untraced contacts that are critical for disease spreading and of great public concern; and establish model-based optimization strategies to prioritize contact tracing procedures toward improving the effectiveness and outcomes of control interventions.

传染病传播的数学模型是管理危险疫情的有力工具。这些模型可以为规划和实施疫苗接种策略、评估旅行禁令的风险和收益以及提高预防活动的有效性奠定基础。然而，传统的建模方法并不能完全捕捉现代社会的国内和国际流动特征，在现代社会中，接触在地理上并不局限于最初爆发的地区，感染可能在一天之内跨越数千英里。该项目将增进对可重构网络上演化的动力系统的基本理解，其中子系统和网络连接在可比较的时间尺度上发生变化。由此产生的数学框架将实现一类新型传染病传播的预测模型。这些模型将有助于保护未受感染的人群并减轻对受灾国家的影响，即使在没有可用的治疗方案（如埃博拉病毒病）的情况下也是如此。更广泛地说，基础理论的进步预计将改变快速重构网络上动力系统的分析、设计和控制。该项目研究部分的补充是促进贫困学生的教育并为经济弱势群体提供服务。该研究项目旨在推动动力系统和复杂网络领域向易于处理的传染病流行数学模型发展。具体来说，该项目将建立一个理论框架，用于研究传染病动态的同时演化及其传播接触网络的形成。该框架将基于活动驱动网络的概念，它可以有效地用于对在一系列时间尺度上随时变网络演变的联系过程进行建模。这种建模范式与传统的连接驱动网络形成对比，在传统的连接驱动网络中，节点之间的链接具有很长的生命周期，导致网络连接动态的时间尺度与过程演化之间的分离。研究团队将通过对全球和本地网络特征的系统分析，寻求了解非理想遏制程序对传染病传播的影响；制定时变网络中的社区检测策略，以识别对疾病传播至关重要且引起公众高度关注的未追踪接触者；建立基于模型的优化策略，优先考虑接触者追踪程序，以提高控制干预措施的有效性和结果。

项目成果

Modeling Human Migration Under Environmental Change: A Case Study of the Effect of Sea Level Rise in Bangladesh

环境变化下的人类迁徙建模：孟加拉国海平面上升影响的案例研究

• DOI: 10.1029/2020ef001931
• 发表时间: 2021-04
• 期刊: Earth's Future
• 作者: De Lellis, Pietro;Ruiz Marín, Manuel;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

Design and Feasibility Study of the Mobile Application StopTheSpread

移动应用程序StopTheSpread的设计和可行性研究

• DOI: 10.1109/access.2020.3022740
• 发表时间: 2020
• 期刊: Ieee Access
• 影响因子: 3.9
• 作者: Nadini M;Richmond S;Huang J;Rizzo A;Porfiri M
• 通讯作者: Porfiri M

On assessing control actions for epidemic models on temporal networks

评估时间网络上流行病模型的控制行动

• DOI: 10.1109/lcsys.2020.2993104
• 发表时间: 2020-10
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Zino, Lorenzo;Rizzo, Alessandro;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

Transfer entropy on symbolic recurrences

符号递归的传递熵

• DOI: 10.1063/1.5094900
• 发表时间: 2019-06
• 期刊: Chaos: An Interdisciplinary Journal of Nonlinear Science
• 作者: Porfiri, Maurizio;Ruiz Marín, Manuel
• 通讯作者: Ruiz Marín, Manuel

Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State

COVID-19 传播的城市决定因素：纽约州三个城市的比较研究

• DOI: 10.1007/s11524-022-00623-9
• 发表时间: 2022-06-06
• 期刊: Journal of Urban Health : Bulletin of the New York Academy of Medicine
• 作者: A. Truszkowska;Maya Fayed;Sihan Wei;Lorenzo Zino;S. Butail;E. Caroppo;Zhong;A. Rizzo;M. Porfiri
• 通讯作者: M. Porfiri