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

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。

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.