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.

这项快速响应研究（Rapid）赠款将支持研究，以提高我们对COVID-19和潜在缓解策略在城市一级的传播的理解，从而促进科学进步并为国家健康和繁荣做出贡献。随着Covid-19的继续传播，不同的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成功的通行设施，但可能对人口的老年人和低收入部分不切实际，以及医院的使用，这为医疗保健系统增加了负担，并可能带来更高传染的风险。预测疾病传播的数学模型对于应采取缓解疫情并减轻人口的行动方案的过程至关重要。但是，这种模型通常会因早期阶段缺乏症状，复杂的流动性模式和有限的测试资源而混淆。该奖项支持对数学模型的基本研究，该数学模型将通过动态和控制方面的进步来克服这些混杂因素。通过明确建模社会和流动性限制，这项研究将有助于增加社区的一般福祉，并减少整个人口的差异。该模型将提供批判性情况的模拟，并包括评估不同的测试政策和缓解行动，从而构成了对涉及遏制和消除流行病的政策制定者的宝贵支持。研究成果将向公众提出，包括卫生专业人员和当局，以在持续的危机中告知公共政策。这项研究将通过基于精细的代理商和数据驱动的模型实时对Covid-19的爆发做出响应，该模型旨在为该病毒在城市层面上提供这种传播和潜在缓解策略的前所未有的见解。该方法将对持续和潜在缓解策略的有效性进行详尽的分析。基于代理的模型将包括COVID-19的特定特征，例如测试的类型和时机，无症状的发生和住院阶段。该框架将基于纽约新罗谢尔的公开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包括在多个空间和时间尺度的基于代理模型的迁移率模式中，以增加预测的粒度。网络理论和数据驱动的控制策略将根据在测试站点收集的可用联系数据库中为涉及主动试验的增强测试协议提供信息。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。

项目成果

The Effect of An Emergency Evacuation on the Spread of COVID19

• DOI: 10.3389/fphy.2020.631264
• 发表时间: 2021-02
• 作者: S. Butail;M. Porfiri

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

• DOI: 10.1063/5.0041993
• 发表时间: 2021-04-01
• 期刊: CHAOS
• 影响因子: 2.9
• 作者: Behring, Brandon M.;Rizzo, Alessandro;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
• 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

Designing the Safe Reopening of US Towns Through High‐Resolution Agent‐Based Modeling

通过基于高分辨率代理的建模设计美国城镇的安全重新开放

• DOI: 10.1002/adts.202100157
• 发表时间: 2021
• 期刊: Advanced Theory and Simulations
• 影响因子: 3.3
• 作者: Truszkowska, Agnieszka;Thakore, Malav;Zino, Lorenzo;Butail, Sachit;Caroppo, Emanuele;Jiang, Zhong‐Ping;Rizzo, Alessandro;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio

Unequal effects of the national lockdown on mental and social health in Italy

• DOI: 10.4415/ann_20_04_13
• 发表时间: 2020-01-01
• 期刊: ANNALI DELL ISTITUTO SUPERIORE DI SANITA
• 影响因子: 2.1
• 作者: Caroppo, Emanuele;De Lellis, Pietro;Porfiri, Maurizio
• 通讯作者: Porfiri, Maurizio