PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics (PandEval)

PIPP 第一阶段:评估大流行期间消息传递和建模的有效性 (PandEval)

基本信息

  • 批准号:
    2200256
  • 负责人:
  • 金额:
    $ 99.95万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-08-01 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

An effective response to fight the spread of a pandemic requires a clear understanding of the complex interactions between biological, environmental and human networks. The COVID19 pandemic revealed both human and systemic failures along this chain. A key takeaway was the need for timely, relevant and actionable information to support effective public messaging and policy making that can impact in-real-life (IRL) outcomes. The COVID19 pandemic also revealed the need for messaging and policy making at a local scale, when national- or state-level approaches might not appropriately address the needs at community scale. Frontline public health officials often had little insight into the individuals that they wished to serve. Decision makers who managed cities or school systems often relied on epidemiological models that did not account for the impact of human beliefs and in-real-life behaviors - e.g., the willingness to wear a mask - on disease transmission. The PandEval project will address these challenges, so as to ultimately increase the trust and confidence in our public health infrastructure. If successful, public health officials will gain insight into the success of (past) messaging campaigns so that they can deliver the right message at the right time. In addition, decision makers will be able to use the outcomes of the epidemiological models, customized to population segments, while planning vaccine rollout, or admitting visitors to congregate living.The innovation of the PandEval project is to rely on curating rich complex multimodal datasets. Social media-based models of community beliefs and attitudes around science skepticism, moral foundations, or the willingness to contribute to the public good, will be developed. Baseline profiles of in-real-life (IRL) behavior tracked by human mobility traces will be computed. Compartmental epidemiological models that account for population characteristics will be customized to account for a diversity of micro-targeted population segments and regions across the US. The PandEval platform will be engineered to measure the effectiveness of community targeted messaging around pandemic mitigation, including recommendations and mandates, and to measure the prediction accuracy of the customized epidemiological models. As the nation faces the potential of endemic COVID19, the PandEval project will create and curate Pand-Index, an index of online social beliefs and in-real-life (IRL) profiles at a national scale. Pand-Index profiles will help individuals to make personalized decisions about social distancing or masking versus working from home.This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).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.
要有效应对大流行病的蔓延,就必须清楚地了解生物、环境和人类网络之间复杂的相互作用。新冠肺炎大流行揭示了这一链条上的人类和系统性失败沿着。一个关键的要点是需要及时、相关和可操作的信息,以支持有效的公共信息传递和政策制定,从而影响现实生活中的结果。2019冠状病毒病大流行还表明,当国家或州一级的方法可能无法适当满足社区规模的需求时,需要在地方规模上进行宣传和制定政策。一线公共卫生官员往往对他们希望服务的个人知之甚少。管理城市或学校系统的决策者往往依赖于流行病学模型,而这些模型没有考虑到人类信仰和现实生活中行为的影响--例如,戴口罩的意愿-对疾病传播的影响。PandEval项目将应对这些挑战,以最终提高对我们公共卫生基础设施的信任和信心。如果成功,公共卫生官员将深入了解(过去)信息宣传活动的成功,以便他们能够在正确的时间传递正确的信息。此外,决策者将能够使用流行病学模型的结果,定制的人群细分,同时规划疫苗推广,或允许游客聚集生活. PandEval项目的创新是依靠策展丰富的复杂的多模态数据集.将开发基于社交媒体的社区信仰模型和围绕科学怀疑论,道德基础或为公共利益做出贡献的意愿的态度。将计算由人类移动轨迹跟踪的现实生活(IRL)行为的基线概况。将对考虑人群特征的房室流行病学模型进行定制,以考虑美国各地微观目标人群和地区的多样性。PandEval平台的设计将用于衡量社区针对大流行病缓解的信息传递的有效性,包括建议和任务,并衡量定制流行病学模型的预测准确性。由于该国面临着地方性COVID 19的潜在威胁,PandEval项目将创建和管理Pand-Index,这是一个全国范围内的在线社会信仰和现实生活(IRL)概况的指数。Pand-Index配置文件将帮助个人做出关于社交距离或屏蔽与在家工作的个性化决策。该奖项由跨部门的流行病预防阶段预测情报(PIPP)计划支持,该计划由生物科学(BIO),计算机信息科学与工程(CISE),工程(ENG)和社会,行为和经济科学(SBE)。该奖项反映了NSF的法定使命,并被认为是值得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的支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Partisan asymmetries in exposure to misinformation.
  • DOI:
    10.1038/s41598-022-19837-7
  • 发表时间:
    2022-09-19
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Rao, Ashwin;Morstatter, Fred;Lerman, Kristina
  • 通讯作者:
    Lerman, Kristina
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Louiqa Raschid其他文献

Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study
COVID-19 大流行期间推特上的公共卫生信息传递:观察性研究
  • DOI:
    10.2196/63910
  • 发表时间:
    2025-01-01
  • 期刊:
  • 影响因子:
    6.000
  • 作者:
    Ashwin Rao;Nazanin Sabri;Siyi Guo;Louiqa Raschid;Kristina Lerman
  • 通讯作者:
    Kristina Lerman
Producing Interoperable Queries for Relational and Object-Oriented Databases
Special issue on data management, analysis, and mining for the life sciences
  • DOI:
    10.1007/s00778-005-0165-5
  • 发表时间:
    2005-09-01
  • 期刊:
  • 影响因子:
    3.800
  • 作者:
    Terry Gaasterland;H. V. Jagadish;Louiqa Raschid
  • 通讯作者:
    Louiqa Raschid
Learning response time for WebSources using query feedback and application in query optimization
  • DOI:
    10.1007/s007780050081
  • 发表时间:
    2000-01-01
  • 期刊:
  • 影响因子:
    3.800
  • 作者:
    Jean-Robert Gruser;Louiqa Raschid;Vladimir Zadorozhny;Tao Zhan
  • 通讯作者:
    Tao Zhan
A semantics for a class of non-deterministic and causal production system programs
  • DOI:
    10.1007/bf00885764
  • 发表时间:
    1994-01-01
  • 期刊:
  • 影响因子:
    0.800
  • 作者:
    Louiqa Raschid;Jorge Lobo
  • 通讯作者:
    Jorge Lobo

Louiqa Raschid的其他文献

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{{ truncateString('Louiqa Raschid', 18)}}的其他基金

Conference: Incorporating Ethics into the Human-Centered Design of AI Solutions
会议:将道德纳入以人为本的人工智能解决方案设计中
  • 批准号:
    2232404
  • 财政年份:
    2022
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
RAPID: Supply Chain Portal to Serve Entrepreneurs Producing Critical Items in Response to COVID-19
RAPID:供应链门户网站,为生产应对 COVID-19 的关键物品的企业家提供服务
  • 批准号:
    2032040
  • 财政年份:
    2020
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
Collaborative Research: Planning Grant: I/UCRC for Assured and SCAlable Data Engineering (CASCADE)
合作研究:规划补助金:I/UCRC 用于有保证和可扩展的数据工程 (CASCADE)
  • 批准号:
    1464644
  • 财政年份:
    2015
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
CI-P: Developing the Next Generation of Community Financial CyberInfrastructure for Monitoring and Modeling Financial Eco-Systems and for Managing Systemic Risk
CI-P:开发下一代社区金融网络基础设施,用于监控和建模金融生态系统以及管理系统性风险
  • 批准号:
    1305368
  • 财政年份:
    2013
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
Workshop on the Next Generation Financial Cyberinfrastructure
下一代金融网络基础设施研讨会
  • 批准号:
    1237476
  • 财政年份:
    2012
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
COLLABORATIVE: ABI Development: Methodology for Pattern Creation, Imprint Validation, and Discovery from the Annotated Biological Web
协作:ABI 开发:模式创建、印记验证和带注释的生物网络发现的方法
  • 批准号:
    1147144
  • 财政年份:
    2012
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Continuing Grant
Workshop on Knowledge Representation and Information Management for Financial Risk Management
金融风险管理知识表示与信息管理研讨会
  • 批准号:
    1033927
  • 财政年份:
    2010
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
III EAGER Collaborative Research: Exploratory Research on the Annotated Biological Web
III EAGER合作研究:带注释的生物网络的探索性研究
  • 批准号:
    0960963
  • 财政年份:
    2009
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
SGER: Addressing the Data Management Challenges of Disaster Information Management Within the Context of a Pilot National Data Center (NDC) for Sri Lanka
SGER:在斯里兰卡试点国家数据中心 (NDC) 的背景下应对灾害信息管理的数据管理挑战
  • 批准号:
    0533986
  • 财政年份:
    2005
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Standard Grant
Collaborative Proposal: II+SEI Mediation Technology for Biological Pipeline Analysis
合作提案:用于生物管道分析的 II SEI 中介技术
  • 批准号:
    0430915
  • 财政年份:
    2005
  • 资助金额:
    $ 99.95万
  • 项目类别:
    Continuing Grant

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