Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology

探险：合作研究：全球普适计算流行病学

• 批准号: 1918784
• 负责人: Alison Galvani
• 金额: $ 90.15万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918784&HistoricalAwards=false
• 关键词: Expeditions; Collaborative; Research; Global; Pervasive

Infectious diseases cause more than 13 million deaths per year worldwide. Rapid growth in human population and its ability to adapt to a variety of environmental conditions has resulted in unprecedented levels of interaction between humans and other species. This rise in interaction combined with emerging trends in globalization, anti-microbial resistance, urbanization, climate change, and ecological pressures has increased the risk of a global pandemic. Computation and data sciences can capture the complexities underlying these disease determinants and revolutionize real-time epidemiology --- leading to fundamentally new ways to reduce the global burden of infectious diseases that has plagued humanity for thousands of years. This Expeditions project will enable novel implementations of global infectious disease computational epidemiology by advancing computational foundations, engineering principles, theoretical understanding, and novel technologies. The innovative tools developed will provide new analytical capabilities to decision makers and result in improved science-based decision making for epidemic planning and response. They will facilitate enhanced inter-agency and inter-government coordination and outbreak response. The team will work closely with many local, regional, national, and international public health agencies and universities to apply and deploy powerful technologies during epidemic outbreaks that can be expected to occur during the course of the project. International scientific networks linked to a comprehensive postdoctoral, graduate and undergraduate student training program will be established. Educational programs to foster interest in and increase understanding of computational science in addressing the complex societal challenges due to pandemics will also be developed. The team, with partners in Asia, Africa, Europe, and Latin America, will produce multidisciplinary scientists with diverse skills related to public health. The novel implementations of this project will be enabled by the development of a rigorous computational theory of spreading and control processes on dynamic multi-scale, multi-layer (MSML) networks, along with tools from AI, machine learning, and social sciences. New techniques resulting from this research will make it possible to develop and apply large-scale simulations of epidemics and social interactions over MSML networks. These simulations, in turn, will provide fundamentally new insights into how to control epidemics. Pervasive computing technologies will be developed to support disease surveillance and real-time response. The computational advances will also be generalizable; that is, they will be applicable to other areas such as cybersecurity, ecology, economics and social sciences. The project will take into account emerging concerns and constraints that include: preserving privacy of individuals and vulnerable groups, enabling model predictions to be interpreted and explained, developing effective interventions under uncertain and unknown network data, understanding strategic and adversarial behaviors of individual agents, and ensuring fairness of the process across the entire population. The research team includes experts from multiple disciplines and will address these societal concerns and constraints in practical, impactful, and novel ways, including the development of computational tools and techniques to support sound, ethical science-based policy pertaining to public health infectious disease epidemiology. Center for Computational Research in Epidemiology (CoRE) at the University of Virginia will be established as a part of the project. CoRE will develop transformative ways to support real-time epidemiology and facilitate improved outbreak response to benefit the society.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.

传染病每年在全球造成超过1300万人死亡。人口的快速增长及其适应各种环境条件的能力导致人与其他物种之间的相互作用水平。这种相互作用的增长与全球化，抗菌抵抗，城市化，气候变化和生态压力的新兴趋势相结合，增加了全球大流行的风险。计算和数据科学可以捕获这些疾病决定因素的复杂性，并彻底改变实时流行病学 - 从根本上导致了减少数千年困扰人类的全球传染病负担的新方法。该探险项目将通过推进计算基础，工程原理，理论理解和新技术来实现全球传染病计算流行病学的新颖实现。开发的创新工具将为决策者提供新的分析能力，并改善基于科学的基于科学的决策，以进行流行计划和响应。它们将促进加强机构间和政府间的协调和爆发反应。该团队将与许多地方，地区，国家和国际公共卫生机构和大学紧密合作，以在流行病爆发期间应用和部署强大的技术，这些技术可以在项目过程中发生。将建立与全面的博士后，研究生和本科生培训计划相关的国际科学网络。还将开发出对计算科学的兴趣和增加对计算科学的了解，以应对大流行病引起的复杂社会挑战的理解。该团队与亚洲，非洲，欧洲和拉丁美洲的合作伙伴将生产具有与公共卫生有关的多种技能的多学科科学家。 该项目的新颖实现将通过开发严格的计算理论，即在动态多尺度，多层（MSML）网络以及AI，机器学习和社会科学的工具上开发和控制过程。这项研究产生的新技术将使通过MSML网络开发和应用大规模的流行病和社交互动模拟。这些模拟反过来将提供有关如何控制流行病的根本新见解。将开发普遍的计算技术来支持疾病监测和实时反应。 计算进步也将是可推广的；也就是说，它们将适用于网络安全，生态，经济学和社会科学等其他领域。该项目将考虑包括：保存个人和弱势群体的隐私，使模型预测能够解释和解释，在不确定且未知的网络数据下制定有效的干预措施，理解单个代理的战略和对抗性行为，以及建立有效的干预措施，并制定有效的干预措施，以及确保整个人群的过程公平。研究小组包括来自多个学科的专家，并将以实用，有影响力和新颖的方式解决这些社会问题和限制，包括开发计算工具和技术，以支持与公共卫生感染性疾病流行病学有关的基于道德科学的政策。弗吉尼亚大学流行病学计算研究中心将作为该项目的一部分建立。核心将开发变革性的方式来支持实时流行病学，并促进改善爆发的反应以使社会受益。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估标准来通过评估来支持的。

项目成果

Infection by SARS-CoV-2 with alternate frequencies of mRNA vaccine boosting

• DOI: 10.1002/jmv.28461
• 发表时间: 2023-02-01
• 期刊: JOURNAL OF MEDICAL VIROLOGY
• 影响因子: 12.7
• 作者: Townsend, Jeffrey P.;Hassler, Hayley B.;Dornburg, Alex
• 通讯作者: Dornburg, Alex

Disease burden among Ukrainians forcibly displaced by the 2022 Russian invasion.

• DOI: 10.1073/pnas.2215424120
• 发表时间: 2023-02-21
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Pandey, Abhishek;Wells, Chad R.;Stadnytskyi, Valentyn;Moghadas, Seyed M.;V. Marathe, Madhav;Sah, Pratha;Crystal, William;Meyers, Lauren Ancel;Singer, Burton H.;Nesterova, Olena;Galvani, Alison P.
• 通讯作者: Galvani, Alison P.