RAPID: Modeling the Coupled Social and Epidemiological Networks that Determine the Success of Behavioral Interventions on Limiting Spread of COVID-19

RAPID:对耦合的社会和流行病学网络进行建模,该网络决定限制 COVID-19 传播的行为干预措施是否成功

基本信息

  • 批准号:
    2028710
  • 负责人:
  • 金额:
    $ 19.89万
  • 依托单位:
  • 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 2022-04-30
  • 项目状态:
    已结题

项目摘要

Abstract:Ideas and viruses can spread in human populations through different forms of interaction. Social distancing is an idea that, when enacted, can lower disease transmission risk and slow the spread of infection in a population. In the case of the current COVID-19 outbreak, in the absence of ready vaccines and medical treatments, social distancing is our best line of defense. The prevalence of social distancing behaviors can depend, however, on the mix of social and geographic communities and their social norms, influencing spread dynamics in schools, social media, work environments, and among friends and family. Members of some social communities (e.g. social media friend groups) may share values and beliefs together without necessarily being in close geographic proximity. Alternatively, people can come into physical proximity—at work, shops, beaches, sporting events— without sharing strongly-held beliefs. Sometimes, even strangers may copy certain visible behaviors, such as wearing protective masks at the grocery store. A pandemic is both a biological and social phenomenon. This work will develop practical tools (models) that predict the interaction between collective behavior and the spatiotemporal dynamics of disease spread. This will enable more accurate predictions of medical resources the population will need over time. Public health measures can target not only individual behavior but also collective behavior, which may require different incentives and nudges, such that public health messaging can be maximally beneficial. Results from the project will also be shared through a public webinar on the role of mathematics in pandemic preparedness.This work to address these gaps will involve two different types of mathematical modeling efforts. The first type will rely on designing a system ordinary differential equations (ODEs) to capture both disease dynamics and social influence. This ODE model will assume that mass action average rates of transition between both disease and belief states are sufficient to gain insight, producing quantitative characterizations to describe how belief dynamics interact with disease prevalence in a community as both progress over time. The second type will rely on designing coupled multi-layer networks in which one layer captures social influence and the other captures physical contact and disease transmission. This model will explore dynamic connections among individuals within each layer, where the strength of contact can shift based on the state and neighbors of the same individual in the other layer. This second model, by focusing on particular network structures will complement the insights about average behaviors gained by the ODE model and provide insight into the different roles individuals may play in shifting community perception and/or spreading infection.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.
翻译后摘要:思想和病毒可以通过不同形式的相互作用在人群中传播。社会距离是一个想法,一旦实施,可以降低疾病传播风险,减缓感染在人群中的传播。 在当前COVID-19疫情爆发的情况下,在缺乏现成疫苗和医疗手段的情况下,保持社交距离是我们最好的防线。然而,社交距离行为的流行可能取决于社会和地理社区及其社会规范的混合,影响学校,社交媒体,工作环境以及朋友和家人之间的传播动态。 一些社交社区(例如社交媒体朋友群)的成员可能会分享价值观和信仰,而不一定在地理上接近。或者,人们可以在工作、商店、海滩、体育赛事中进行物理接触,而不需要分享坚定的信仰。有时,即使是陌生人也可能会模仿某些可见的行为,例如在杂货店戴防护口罩。 大流行病既是一种生物现象,也是一种社会现象。这项工作将开发实用的工具(模型),预测集体行为和疾病传播的时空动态之间的相互作用。这将使人们能够更准确地预测随着时间的推移所需的医疗资源。公共卫生措施不仅可以针对个人行为,也可以针对集体行为,这可能需要不同的激励和推动,以便公共卫生信息可以最大限度地发挥作用。该项目的成果还将通过一个关于数学在大流行防范中的作用的公开网络研讨会进行分享。解决这些差距的工作将涉及两种不同类型的数学建模工作。第一种类型将依赖于设计一个系统的常微分方程(ODE),以捕捉疾病的动态和社会影响。这个ODE模型将假设疾病和信念状态之间的大规模行动平均转换率足以获得洞察力,产生定量表征来描述信念动态如何与社区中的疾病流行率相互作用,因为两者都随着时间的推移而发展。第二种类型将依赖于设计耦合的多层网络,其中一层捕获社会影响,另一层捕获身体接触和疾病传播。该模型将探索每一层中个体之间的动态联系,其中联系的强度可以根据另一层中同一个体的状态和邻居而变化。第二个模型通过关注特定的网络结构,将补充ODE模型获得的关于平均行为的见解,并提供对个人在改变社区认知和/或传播感染方面可能发挥的不同作用的见解。该奖项反映了NSF的法定使命,并且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

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Diversity in valuing social contact and risk tolerance leading to the emergence of homophily in populations facing infectious threats
重视社会接触和风险承受能力的多样性导致面临感染威胁的人群出现同质性
  • DOI:
    10.1103/physreve.105.044315
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    Young, Matthew J.;Silk, Matthew J.;Pritchard, Alex J.;Fefferman, Nina H.
  • 通讯作者:
    Fefferman, Nina H.
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Nina Fefferman其他文献

Vital rate sensitivity analysis as a tool for assessing management actions for the desert tortoise
  • DOI:
    10.1016/j.biocon.2009.06.025
  • 发表时间:
    2009-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    J. Michael Reed;Nina Fefferman;Roy C. Averill-Murray
  • 通讯作者:
    Roy C. Averill-Murray
DialectDecoder: Human/machine teaming for bird song classification and anomaly detection
DialectDecoder:人机协作进行鸟鸣分类和异常检测
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    5.1
  • 作者:
    Brittany Story;Patrick Gillespie;Graham Derryberry;Elizabeth Derryberry;Nina Fefferman;Vasileios Maroulas
  • 通讯作者:
    Vasileios Maroulas

Nina Fefferman的其他文献

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{{ truncateString('Nina Fefferman', 18)}}的其他基金

PIPP Phase I: Predicting Emergence in Multidisciplinary Pandemic Tipping-points (PREEMPT)
PIPP 第一阶段:预测多学科流行病临界点的出现 (PREEMPT)
  • 批准号:
    2200140
  • 财政年份:
    2022
  • 资助金额:
    $ 19.89万
  • 项目类别:
    Standard Grant
Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems
协作研究:多尺度、复杂、动态系统中出现前和罕见事件的预测研讨会
  • 批准号:
    2114651
  • 财政年份:
    2021
  • 资助金额:
    $ 19.89万
  • 项目类别:
    Standard Grant
RAPID: Modeling Zika Control Effectiveness with Feedback in Risk Perception and Associated Demand across Scales of Intervention
RAPID:通过风险感知反馈和跨干预规模的相关需求来建模寨卡控制有效性
  • 批准号:
    1640951
  • 财政年份:
    2016
  • 资助金额:
    $ 19.89万
  • 项目类别:
    Standard Grant
EAGER: Collaborative: Algorithmic Framework for Anomaly Detection in Interdependent Networks
EAGER:协作:相互依赖网络中异常检测的算法框架
  • 批准号:
    1646890
  • 财政年份:
    2016
  • 资助金额:
    $ 19.89万
  • 项目类别:
    Standard Grant
RAPID: Collaborative Research: Learning about Infectious Diseases through Online Participation in a Virtual Epidemic
RAPID:协作研究:通过在线参与虚拟流行病来了解传染病
  • 批准号:
    1508981
  • 财政年份:
    2015
  • 资助金额:
    $ 19.89万
  • 项目类别:
    Standard Grant

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