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: 2115164
• 负责人: John Yin
• 金额: $ 1.56万
• 依托单位: University of Wisconsin-Madison
• 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=2115164&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.

在过去一年，持续的COVID-19大流行严重扰乱了我们星球上人类居民的生计，感染了超过8500万人，并造成近200万人死亡。可以采取什么行动来尽量减少这一流行病的严重性（以及过去几十年来在它之前的其他流行病，如寨卡病毒，SARS和埃博拉病毒）？回顾过去，许多行动本可以发挥关键作用：对潜在的动物到人的感染溢出进行环境监测，建立快速疫苗开发和最佳部署和分配的管道，设计数据科学工具以准确预测轨迹，快速和适应性的症状监测和行为跟踪，设计和定时有效的干预措施，培训易受感染的个人采取必要措施，以抑制传染性病原体的传播等。我们吸取了哪些教训，在知识、方法、技术和政策方面还存在哪些差距？研究人员提议召开为期两天的多学科国家研讨会，通过新技术预测有毒实体的出现（PREVENT），以开始解决这些和相关的挑战。作为一个整体，高度跨学科的组织团队在本次研讨会所涉及的主题的各个方面都有丰富的经验。弥合已知（甚至可能是可知）领域的根本性差距，需要的协调远远超出工具共享、标准化或方法和数据交流的范围;这些定义了流行病预测之外复杂问题的更广泛社会挑战。这次会议将有助于实现协调的团队科学工作，无论是在小型团队还是大型团队中，都可以帮助将不同的群体聚集在一起，包括将公众作为公民科学家。科学和工程以跨学科整合的方式进行，以便社区的努力最适合（并且能够灵活地）应对和预防新的流行病。研讨会围绕四个主题和观点展开：分子，生理，人口/流行病学和端端/多尺度。 拟议的会议将为社区提供一个宝贵的机会，开始建立必要的趋同。将采用全体会议、简短会谈、小组讨论和小型分组思考会议相结合的方式来帮助实现这些目标。基于几个重要原因，预测性智能在流行病预防方面将受益于融合计算、科学和工程见解以及传统的学科专业知识库。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。