PIPP Phase I: Computational Theory of the Co-evolution of Pandemics, (Mis)information, and Human Mindsets and Behavior

PIPP 第一阶段：流行病、（错误）信息以及人类心态和行为共同进化的计算理论

• 批准号: 2200112
• 负责人: Peter Pirolli
• 金额: $ 100万
• 依托单位: Florida Institute for Human and Machine Cognition, Inc.
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200112&HistoricalAwards=false
• 关键词: PIPP; Phase; Computational; Theory; Co

Epidemiological models are used to predict the spread of highly contagious and lethal diseases such as COVID-19. Public health officials use such models to inform pandemic response policies and advisories. Yet these models require a rigorous scientific foundation about human psychology to better predict people’s responses to information and policies about pandemics. The recent COVID-19 pandemic illustrates the central role of human decision making and behavior in the spread of such a transmissible disease. People’s decisions regarding social isolation, social distancing, mask wearing, hand washing, and vaccination are correlated with the rate at which the COVID-19 virus spreads or the seriousness of getting infected. People have different individual mindsets, and these can vary across different regions and subgroups, so different groups of people respond differently to messaging and mandates and those responses change over time. There is also an ongoing scientific debate about the degree to which pandemic information or misinformation, or the perceived credibility of information sources, influences the degree to which people change their behavior. To address these scientific needs, this project involves activities to develop a multidisciplinary research core and agenda and to develop a strong plan for a cohesive research center for Predictive Intelligence for Pandemic Prevention. The activities include exploratory research on computational models of human psychology, information flow and influence, and resulting pandemic transmission. The project will also support the training and mentoring of graduate students who represent the next generation of researchers tackling these global challenges.This project uses computational theories and models to examine the fundamental interdependent evolution of infection, behavior, and information at multiple levels and drawing upon multiple disciplines in order to support improved pandemic intelligence, prediction, explanation, and countermeasures. The project is organized into (1) interdisciplinary, strategic research thrusts to Accelerate Convergent Science towards the Grand Challenge, (2) three invitational meetings to draw in diverse researchers to address focal research topics and research questions, to fill in gaps in the Research Challenges, and develop a strong research and education agenda for a cohesive PIPP center, and (3) Pilot Studies to Demonstrate Feasibility of integrated computational models of information, human psychology, and pandemic transmission. For the pilot research, a multidisciplinary team combines empirical assessments with computational cognitive models in an agent-based modeling system. For data the investigators draw on vaccination discussions in mass media, Twitter, geolocated timeseries data on vaccination rates, infection, death and recovery rates, state and national mandates regarding COVID-19 policies about vaccination and mask wearing from February 2020 through December 2021 in the United States. These data will be segmented by state and major cities within those states. 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.

流行病学模型用于预测COVID-19等高传染性和致命性疾病的传播。公共卫生官员使用这些模型为大流行应对政策和咨询提供信息。然而，这些模型需要关于人类心理的严格科学基础，才能更好地预测人们对有关流行病的信息和政策的反应。最近的COVID-19大流行说明了人类决策和行为在这种传染病传播中的核心作用。人们在社会隔离、保持社会距离、戴口罩、洗手和接种疫苗方面的决定与COVID-19病毒的传播速度或感染的严重程度相关。人们有不同的个人心态，这些心态在不同的地区和子群体中会有所不同，所以不同的群体对信息和命令的反应不同，这些反应会随着时间的推移而变化。关于大流行信息或错误信息，或信息来源的可信度，在多大程度上影响人们改变其行为的程度，也正在进行一场科学辩论。为了满足这些科学需求，本项目涉及制定多学科研究核心和议程的活动，并为一个具有凝聚力的预防大流行病预测情报研究中心制定一个强有力的计划。这些活动包括对人类心理、信息流和影响以及由此产生的大流行病传播的计算模型进行探索性研究。该项目还将支持研究生的培训和指导，这些研究生代表着应对这些全球挑战的下一代研究人员。本项目利用计算理论和模型，在多个层面上考察感染、行为和信息的基本相互依存演变，并借鉴多个学科，以支持改进大流行情报、预测、解释和对策。该项目组织为(1)跨学科的战略研究重点，以加速科学向大挑战的融合；(2)三次邀请会议，吸引不同的研究人员解决重点研究课题和研究问题，填补研究挑战中的空白，并为一个有凝聚力的PIPP中心制定强有力的研究和教育议程；(3)试点研究，以证明集成信息计算模型的可行性。人类心理学和流行病传播。在试点研究中，一个多学科团队将经验评估与基于主体的建模系统中的计算认知模型相结合。对于数据，调查人员利用了大众媒体、推特上的疫苗接种讨论、关于疫苗接种率、感染、死亡和恢复率的地理时序数据，以及美国2020年2月至2021年12月期间有关COVID-19疫苗接种和戴口罩政策的州和国家规定。这些数据将按州和州内的主要城市进行分割。该奖项由流行病预防的跨部门预测情报第一阶段（PIPP）计划提供支持，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）委员会共同资助。该奖项反映了美国国家科学基金会的法定使命，并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。