CAREER: Designing a Multi-Scale Framework for Trait Variation in Epidemiological Dynamics

COVID-19大流行表明了传染病可能对现代社会造成的破坏。解读疾病的动态对于更早和更有效地采取干预措施以减缓疾病传播至关重要。为了充分理解这些动态，必须将在多个空间和时间尺度上运行的系统中的信息以及通过反馈演变的信息结合起来。该研究项目旨在开发一个数学框架，以联合收割机和分析这些信息，以进一步了解传染病的传播，为正在进行的和未来的疾病爆发提供见解。研究结果也适用于更普遍的生态学和免疫学中的多尺度问题。教育部分将用于培养跨学科科学家的多样化一代，建设STEM劳动力的能力。通过集中的短期课程，旨在提供定量技术和跨学科沟通的具体，相关的技能，在数学和生命科学的研究生将学会有效地合作和沟通。对本科生和研究生的培训和指导将通过重点研究项目和获得翻译技能的机会进行，例如制定和实施基于项目的工作并参与外联活动。此外，互动演示将通过接触到尖端的数学生物学研究吸引中学生。当前的COVID-19疫情凸显了可解释的定量模型的重要性，该模型将机制与数据联系起来，同时考虑到可变性。然而，传染病的动力学仍然不完全理解，部分原因是缺乏异质性考虑的免疫学，生态学和流行病学方面的模型。复杂的非线性反馈来自异质性;因此，需要新的定量框架来更好地理解和控制传染病。该项目建立在生态学的工作，涉及整体投影模型，以开发一个框架，将基于特征的变化。该框架旨在描述人群中免疫力的发展，从而确定随时间推移的疾病风险。结果将有助于监测传染病，选择干预措施，并为公共政策提供信息。教育部分旨在加强量化扫盲，以帮助培养更多的跨学科劳动力。一组研究生，跨应用，数学和计算学科，将介绍基本的定量技能，如统计分析，模型构建，参数估计，数据可视化和编码。参与者将使用基于项目的方法与传染病动力学相关的例子联系起来。该奖项反映了NSF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。

The COVID-19 pandemic demonstrates the devastation that infectious diseases can leave on modern society. Deciphering the dynamics of disease is essential to earlier and more effective interventions to mitigate disease spread. To fully understand these dynamics, information across systems that operate on multiple spatial and time scales, as well as evolving via feedback, must be combined. This research project aims to develop a mathematical framework to combine and analyze such information to further the understanding of infectious disease spread, providing insight for ongoing and future disease outbreaks. The results are also intended to be applicable to multi-scale questions in ecology and immunology more generally. The educational component will serve to train a diverse generation of interdisciplinary scientists, building the capacity of the STEM workforce. Through focused short courses aimed at providing specific, relevant skills in quantitative techniques and cross-disciplinary communication, graduate students in the mathematical and life sciences will learn to collaborate and communicate effectively. Training and mentoring of undergraduate and graduate students will take place through focused research projects and opportunities to garner translational skills, for example developing and implementing project-based work and participating in outreach activities. Additionally, interactive presentations will engage middle school students through exposure to cutting-edge mathematical biology research. The current COVID-19 pandemic highlighted the importance of interpretable, quantitative models that link mechanisms with data while accounting for variability. However, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in models of immunological, ecological, and epidemiological aspects. Complex, non-linear feedbacks arise from heterogeneities; thus, novel quantitative frameworks are needed to better understand and control infectious disease. This project builds on work in ecology involving integral projection models to develop a framework incorporating trait-based variation. This framework aims to describe development of immunity in a population and, thus, determine disease risk over time. Results will be useful for monitoring infectious disease, selecting interventions, and informing public policy. The educational component aims to strengthen quantitative literacy to help produce a more interdisciplinary workforce. A group of graduate students, across applied, mathematical, and computational disciplines, will be introduced to fundamental quantitative skills such as statistical analyses, model building, parameter estimation, data visualization, and coding. Participants will connect with this material using a project-based approach with examples related to infectious disease dynamics.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.