Collaborative Research: National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT)

合作研究：利用新技术预测有毒实体出现的全国研讨会（预防）

• 批准号: 2115126
• 负责人: B Aditya Prakash
• 金额: $ 6.61万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2115126&HistoricalAwards=false
• 关键词: Collaborative; Research; National; Symposium; PRedicting

In the past year, the ongoing COVID-19 pandemic has severely disrupted the livelihoods of our planet’s human inhabitants, infecting over 85 million individuals, and causing nearly 2 million deaths. What actions might have been taken to minimize the severity of this pandemic (and others before it in the past decades such as Zika, SARS and Ebola)? In retrospect, many actions could have played key roles: environmental monitoring for potential animal-to-human infection spillovers, establishment of pipelines for rapid vaccine development and optimal deployment and distribution, designing data-science tools to accurately forecast trajectories, fast and adaptive syndromic surveillance and behavior tracking, designing and timing effective interventions, training susceptible individuals for measures needed to inhibit the spread of infectious agents, and others. What lessons have been learned and what gaps in our knowledge, methodologies, technologies, and policies remain? The investigators propose a two-day multi-disciplinary National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT) to begin to address these and related challenges. As a whole the highly interdisciplinary organizing team has significant experience in various aspects of the topics touched upon by this symposium. Bridging fundamental gaps in what is known (and perhaps even what is knowable) can require coordination that goes far beyond sharing of instruments, standardization, or the exchange of methods and data; these define broader societal challenges of complex problems beyond pandemic prediction. This meeting will help enable coordinated team-science efforts that can assist in bringing disparate groups together, whether in small teams or large teams, including bringing in the public as citizen scientists.Key in fostering convergence for predictive intelligence for pandemic prevention will be co-envisioning computing, science and engineering in ways that are integrated across disciplines so that community efforts are optimally suited to (and nimbly able to) respond to and prevent new pandemics. The symposium has been structured around four themes and perspectives: Molecular, Physiological, Population/Epidemiological and End-end/Multi-scale. The proposed meeting will provide a valuable opportunity for the community to begin to build the necessary convergence. A combination of plenary talks, short talks, panel discussions and small breakout thought sessions will be used to help achieve these aims. For several significant reasons, predictive intelligence for pandemic prevention stands to benefit by drawing upon convergent computation, science and engineering insights alongside traditional disciplinary repositories of expertise.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.

在过去的一年里，持续的新冠肺炎大流行严重扰乱了地球上人类居民的生计，感染了8500多万人，导致近200万人死亡。本可以采取什么行动来最大限度地降低这场大流行(以及过去几十年的寨卡、SARS和埃博拉等其他疫情)的严重性？回过头来看，许多行动本来可以发挥关键作用：对潜在的动物对人类的感染溢出进行环境监测，为快速开发疫苗和优化部署和分发建立管道，设计准确预测轨迹的数据科学工具，快速和自适应的综合症监测和行为跟踪，设计有效的干预措施并确定有效干预的时间，培训易感人群采取必要措施抑制感染源的传播，等等。我们吸取了哪些教训，在知识、方法、技术和政策方面仍存在哪些差距？研究人员提议召开一次为期两天的多学科全国研讨会，以预测新技术(预防)产生的有毒实体，以开始应对这些和相关的挑战。总体而言，高度跨学科的组织团队在本次研讨会涉及的主题的各个方面都有丰富的经验。弥合已知(甚至可能是已知的)方面的根本差距可能需要远远超出共享工具、标准化或方法和数据交换的协调；这些定义了大流行预测之外的复杂问题的更广泛的社会挑战。这次会议将有助于实现协调的团队科学努力，有助于将不同的群体聚集在一起，无论是以小团队还是大团队的形式，包括引入公众作为公民科学家。促进大流行预防的预测情报融合的关键将是以跨学科整合的方式共同设想计算、科学和工程，以便社区的努力最适合(并能够灵活地)应对和预防新的流行病。研讨会围绕四个主题和视角展开：分子、生理、人口/流行病学和末端/多尺度。拟议中的会议将为社会提供一个宝贵的机会，开始建立必要的衔接。将结合全体会谈、简短会谈、小组讨论和小型分组思考会议来帮助实现这些目标。由于几个重要原因，大流行预防的预测情报将受益于融合计算、科学和工程洞察力以及传统的学科专家库。这一奖项反映了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。