Managing Epidemics by Managing Mobility

通过管理流动性来管理流行病

• 批准号: 2033580
• 负责人: Samitha Samaranayake
• 金额: $ 77.17万
• 依托单位: Cornell 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: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033580&HistoricalAwards=false
• 关键词: Managing; Epidemics; Mobility

This NSF grant will study the coupling between personal mobility and the spread of infectious disease. Recent experiences with COVID-19 have highlighted the importance of directly modeling transportation flows within epidemiological models and understanding the impacts of complex, local-scale travel patterns and their network effects. In particular, the project will help address questions of the following nature: i) Which communities are most likely to accelerate disease propagation throughout the network? ii) Which recurrent travel patterns are most likely to become disease vectors? iii) What combination of social-distancing and travel restriction measures are needed to safely reactivate a region? iv) Where should preventive screening be administered (when resources are limited) to minimize contagion throughout the network? The project aims to engage with public health experts and deliver implementations of the models in publicly accessible forms to support policy maker engagement. The technical contributions of the project include : i) development of enhanced spatial meta-population susceptible-exposed-infectious-recovered (SEIR) models that explicitly capture complex high fidelity travel patterns and their nuanced interactions with the dynamics of the disease; ii) extensions that can go beyond the traditional notion of a meta-population to explicitly model infections that occur en-route (e.g. while using a transit system); iii) network analysis tools for strategic planning that can explain the implications on contagion of different mobility patterns and mitigation strategies that alter these patterns; iv) optimal control strategies for managing outbreaks and planning for the reopening phase using advanced analytical and numerical methods; and v) data-driven Bayesian auto-calibration techniques that utilize a combination of real-time case data, historic data, and local expert knowledge. The team will build an open source simulation tool that allows the outputs of this project to be accessible, reproducible and extensible by others.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.

这项基金将研究个人流动性与传染病传播之间的联系。最近的COVID-19经验凸显了在流行病学模型中直接建模交通流以及了解复杂的地方规模旅行模式及其网络效应的重要性。特别是，该项目将有助于解决以下性质的问题：i）哪些社区最有可能加速疾病在整个网络中的传播？（ii）哪些经常性旅行模式最有可能成为疾病媒介？iii）需要什么样的社交距离和旅行限制措施的组合才能安全地重新激活一个地区？㈣应在何处进行预防性筛查（当资源有限时），以尽量减少整个网络的传染？该项目旨在与公共卫生专家合作，并以公众可访问的形式提供模型的实施，以支持政策制定者的参与。 该项目的技术贡献包括：（一）开发增强的空间元人群易感-暴露-感染-康复（SEIR）模型，明确捕捉复杂的高保真旅行模式及其与疾病动态的微妙相互作用; ii）可以超越元群体的传统概念的扩展，以明确地对途中发生的感染进行建模（例如在使用过境系统时）; ㈢战略规划网络分析工具，可解释不同流动模式对传染的影响以及改变这些模式的缓解战略; ㈣利用先进的分析和数值方法，制定最佳控制战略，以管理疫情和规划重新开放阶段;以及v）数据驱动的贝叶斯自动校准技术，其利用实时病例数据、历史数据和本地专家知识的组合。该团队将建立一个开源的模拟工具，使该项目的输出可以被其他人访问，复制和扩展。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

A measure model for the spread of viral infections with mutations

• DOI: 10.3934/nhm.2022015
• 发表时间: 2022-03
• 期刊: Networks Heterog. Media
• 作者: Xiaoqian Gong;B. Piccoli

Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks

• DOI: 10.1145/3543507.3583452
• 发表时间: 2023-04
• 期刊: Proceedings of the ACM Web Conference 2023
• 作者: Yue Hu;Yuhang Zhang;Yanbing Wang;D. Work

Control of COVID-19 outbreak using an extended SEIR model

• DOI: 10.1142/s0218202521500512
• 发表时间: 2021-11-01
• 期刊: MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES
• 影响因子: 3.5
• 作者: McQuade, Sean T.;Weightman, Ryan;Piccoli, Benedetto
• 通讯作者: Piccoli, Benedetto

Coupling compartmental models with Markov chains and measure evolution equations to capture virus mutability

将区室模型与马尔可夫链耦合并测量进化方程以捕获病毒的变异性

• DOI: 10.1142/s0218202522500506
• 发表时间: 2022
• 期刊: Mathematical Models and Methods in Applied Sciences
• 影响因子: 3.5
• 作者: Weightman, Ryan;Sbarra, Anthony;Piccoli, Benedetto
• 通讯作者: Piccoli, Benedetto