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.

这项快速反应研究（RAPID）拨款将支持研究，以提高我们对COVID-19传播的理解以及城市层面的潜在缓解策略，促进科学进步并为国家健康和繁荣做出贡献。随着COVID-19持续蔓延，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成功但对老年人和低收入人群可能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，并可能带来更高的传染风险。预测疾病传播的数学模型至关重要，可以让地方和全球决策者了解为缓解疫情和救济民众而应采取的行动方针。然而，这种模型往往被早期症状的缺乏、复杂的移动模式和有限的测试资源所混淆。该奖项支持对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淆因素。通过明确建模社会和流动性限制，这项研究将有助于提高社区的总体福祉，减少人口之间的差距。该模型将模拟关键的假设情景，并将包括评价不同的测试政策和缓解行动，从而为参与遏制和消除这一流行病的决策者提供宝贵的支持。研究成果将向公众展示，包括卫生专业人员和当局，为持续危机中的公共政策提供信息。该研究将通过基于精细分辨率代理和数据驱动的模型来真实的应对COVID-19爆发，旨在提供这种病毒在城市层面的传播和潜在缓解策略的前所未有的洞察力。该方法将对正在进行的和潜在的缓解战略的有效性进行透彻的假设分析。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、无症状发生和住院阶段。该框架将以纽约罗谢尔公开提供的人口普查和地理参考数据为基础。 与理性和非理性因素相关的社会行为将被包括在基于代理的模型的多个空间和时间尺度的移动模式中，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协议提供信息，这些协议涉及在测试站点收集的可用联系数据库的基础上进行的主动试验。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

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

On Assessing Control Actions for Epidemic Models on Temporal Networks

• DOI: 10.1109/lcsys.2020.2993104
• 发表时间: 2020-05
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Lorenzo Zino;A. Rizzo;M. Porfiri

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