RAPID/Collaborative Research: Agent-based Modeling Toward Effective Testing and Contact-tracing During the COVID-19 Pandemic

快速/协作研究：基于代理的建模，以在 COVID-19 大流行期间实现有效的测试和接触者追踪

• 批准号: 2027990
• 负责人: Maurizio Porfiri
• 金额: $ 16.11万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027990&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Agent; based

This Rapid Response Research (RAPID) grant will support research that will improve our understanding of the spread of COVID-19 and potential mitigation strategies at the city level, promoting scientific progress and contributing to national health and prosperity. As COVID-19 continues to spread, the effectiveness of different testing strategies and predictive models are brought into question. Testing strategies include the use of drive-through facilities that have found success elsewhere but may prove impractical for elderly and low-income sections of the population, and the use of hospitals, which adds further burden to the healthcare system and may carry the risk of higher contagion. Mathematical models that forecast the spread of the disease are of paramount importance to inform local and global policy makers on the course of action that should be undertaken to mitigate the outbreak and give relief to the population. However, such models are often confounded by the absence of symptoms in early stages, complex mobility patterns, and limited testing resources. This award supports fundamental research toward a mathematical model that will overcome these confounding factors, through advancements in dynamics and control. By explicitly modeling social and mobility constraints, this research will help increase the general well-being of communities and reduce disparities across the population. The model will afford the simulation of critical what-if scenarios and will include the evaluation of different testing policies and mitigation actions, thereby constituting a valuable support to policy makers involved in the containment and eradication of the epidemic. Research outcomes will be presented to the public, including health professionals and authorities to inform public policy in the ongoing crisis.The research will respond to COVID-19 outbreak in real time through a fine-resolution agent-based and data-driven model that aims at providing unprecedented insight in the spread and potential mitigation strategies of this virus at the city level. The approach will afford thorough what-if analysis on the effectiveness of ongoing and potential mitigation strategies. The agent-based model will include COVID-19 specific features, such as the type and timing of testing, asymptomatic occurrence, and hospitalization stages. The framework will be grounded in publicly available census and geo-referred data from New Rochelle, New York. Social behavior associated with rational and irrational factors will be included in the mobility patterns of the agent-based model at multiple spatial and temporal scales to increase the granularity of the predictions. Network-theoretic and data-driven control strategies will inform enhanced testing protocols involving active trials on the basis of available contact databases collected at testing sites.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.

这笔快速响应研究 (RAPID) 拨款将支持研究，以提高我们对 COVID-19 传播和城市层面潜在缓解策略的了解，促进科学进步并为国民健康和繁荣做出贡献。随着 COVID-19 的继续传播，不同测试策略和预测模型的有效性受到质疑。测试策略包括使用在其他地方取得了成功但可能对老年人和低收入群体来说不切实际的免下车设施，以及使用医院，这进一步增加了医疗保健系统的负担，并可能带来风险传染性更高。预测疾病传播的数学模型对于告知当地和全球政策制定者应采取哪些行动来减轻疫情并为民众提供救济至关重要。然而，此类模型常常因早期阶段没有症状、复杂的移动模式和有限的测试资源而令人困惑。该奖项支持数学模型的基础研究，该模型将通过动力学和控制方面的进步克服这些混杂因素。通过明确地模拟社会和流动性限制，这项研究将有助于提高社区的总体福祉并减少人口之间的差距。该模型将模拟关键的假设情景，并将包括对不同检测政策和缓解行动的评估，从而为参与遏制和根除疫情的政策制定者提供宝贵的支持。研究成果将向公众展示，包括卫生专业人员和当局，以便为当前危机中的公共政策提供信息。该研究将通过基于精细代理和数据驱动的模型实时应对 COVID-19 爆发，该模型旨在提供有关该病毒在城市一级的传播和潜在缓解策略的前所未有的见解。该方法将对正在进行的和潜在的缓解策略的有效性进行彻底的假设分析。基于代理的模型将包括 COVID-19 的特定特征，例如检测的类型和时间、无症状发生情况和住院阶段。该框架将基于纽约州新罗谢尔公开的人口普查和地理参考数据。 与理性和非理性因素相关的社会行为将被包含在基于代理的模型在多个空间和时间尺度上的移动模式中，以增加预测的粒度。网络理论和数据驱动的控制策略将为增强的测试方案提供信息，其中涉及基于在测试站点收集的可用联系数据库的主动试验。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技术进行评估，被认为值得支持。更广泛的影响审查标准。

项目成果

Exploring a COVID‐19 Endemic Scenario: High‐Resolution Agent‐Based Modeling of Multiple Variants

探索 COVID-19 流行情况：基于高分辨率代理的多变体建模

• DOI: 10.1002/adts.202200481
• 发表时间: 2022-11
• 期刊: Advanced Theory and Simulations
• 影响因子: 3.3
• 作者: Truszkowska, Agnieszka;Zino, Lorenzo;Butail, Sachit;Caroppo, Emanuele;Jiang, Zhong‐Ping;Rizzo, Alessandro;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

Quantifying the role of the COVID-19 pandemic in the 2020 U.S. presidential elections

量化 COVID-19 大流行在 2020 年美国总统选举中的作用

• DOI: 10.1140/epjs/s11734-021-00299-3
• 发表时间: 2021-01
• 期刊: The European Physical Journal Special Topics
• 作者: De Lellis, Pietro;Ruiz Marín, Manuel;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

The Impact of Deniers on Epidemics: A Temporal Network Model

否认者对流行病的影响：时间网络模型

• DOI: 10.1109/lcsys.2022.3219772
• 发表时间: 2023-01
• 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

How adherence to public health measures shapes epidemic spreading: A temporal network model

遵守公共卫生措施如何影响流行病传播：时间网络模型

• DOI: 10.1063/5.0041993
• 发表时间: 2021-04
• 期刊: Chaos: An Interdisciplinary Journal of Nonlinear Science
• 作者: Behring, Brandon M.;Rizzo, Alessandro;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

Analysis of lockdown perception in the United States during the COVID-19 pandemic

COVID-19 大流行期间美国的封锁认知分析

• DOI: 10.1140/epjs/s11734-021-00265-z
• 发表时间: 2022
• 期刊: The European Physical Journal Special Topics
• 作者: Surano FV;Porfiri M;Rizzo A
• 通讯作者: Rizzo A