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

有效应对大流行的蔓延需要清楚地了解生物、环境和人类网络之间的复杂相互作用。COVID19大流行揭示了这一链条上的人为和系统性失误。一个关键的要点是需要及时、相关和可操作的信息，以支持可能影响现实生活(IRL)结果的有效公共信息和政策制定。COVID19大流行还表明，当国家或州一级的方法可能无法适当地解决社区规模的需求时，需要在地方范围内传递信息和制定政策。一线公共卫生官员往往对他们希望服务的个人缺乏洞察力。管理城市或学校系统的决策者往往依赖于流行病学模型，这些模型没有考虑到人类信仰和现实生活中的行为--例如，是否愿意戴口罩--对疾病传播的影响。PandEval项目将应对这些挑战，从而最终增加人们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(过去的)信息传播活动的成功，以便他们能够在正确的时间传递正确的信息。此外，决策者将能够在规划疫苗推出或允许游客聚集生活时使用针对人群部分定制的流行病学模型的结果。PandEval项目的创新在于依赖于管理丰富的复杂多模式数据集。将开发基于社交媒体的社区信念和态度模式，围绕科学怀疑论、道德基础或为公共利益做出贡献的意愿。将计算由人类活动轨迹跟踪的真实生活(IRL)行为的基线配置文件。考虑到人口特征的地区性流行病学模型将被定制，以考虑美国各地不同的微目标人群部分和地区。PandEval平台将被设计成衡量围绕大流行缓解的社区定向信息的有效性，包括建议和任务，并衡量定制流行病学模型的预测准确性。随着美国面临流行COVID19的可能性，PandEval项目将创建和策划Pand-Index，这是一个全国性的在线社会信仰和现实生活(IRL)简介的索引。Pand-Index配置文件将帮助个人就社交距离或掩饰与在家工作做出个性化决定。该奖项由跨部门大流行预防第一阶段预测情报(PIPP)计划支持，该计划由生物科学(BIO)、计算机信息科学和工程(CESE)、工程(ENG)和社会、行为和经济科学(SBE)主管共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.