Collaborative Research: Understanding Stochastic Spatiotemporal Dynamics of Epidemic Spread to Improve Control Interventions - From COVID-19 to Future Pandemics

合作研究：了解流行病传播的随机时空动态以改进控制干预措施 - 从 COVID-19 到未来的大流行

• 批准号: 2140420
• 负责人: Manish Kumar
• 金额: $ 21.5万
• 依托单位: University of Cincinnati Main Campus
• 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: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140420&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Stochastic; Spatiotemporal

This grant will support research that will contribute new scientific knowledge related to how uncertainties in both human behavior and transmission characteristics of a causative pathogen (such as the novel coronavirus in the case of COVID-19) influence the spread of an epidemic, and how the new knowledge thus obtained about epidemic spread can contribute to interventional public health policy measures to effectively mitigate and control an epidemic. The research will advance both the science of predicting epidemic spread as well as national prosperity by enhancing national preparedness for early and effective mitigation of potential future epidemic outbreaks. Mathematical and computational models that can accurately predict an epidemic spread across geographical regions over specified periods of time are critical precursors to developing effective interventions for mitigation such as social-distancing measures and vaccination campaigns (when vaccines become available). However, the limitations of existing predictive models, as evident during the COVID-19 outbreak in the US, underscore the need for new knowledge in this area. This award supports fundamental research to develop novel predictive models of epidemic spread and also to validate model predictions against the extensive COVID-19 spread data only now available. This research involves multiple disciplines including the mathematical theory of partial differential equations, stochastic analysis, control theory, and epidemiology and the results will likely have broader significance in the study of rare-event dynamics in areas such as ecology, climate science and wildfire propagation. Moreover, this cross-disciplinary project, a collaborative effort involving multiple institutions, will broaden the participation of underrepresented groups in research and training, and also advance science and engineering education.The research will advance the fundamental knowledge of how uncertainties, both in human behavior and pathogen characteristics, influence spatiotemporal, stochastic epidemic dynamics and also yield a control-theoretic framework to analyze interventions for mitigation. Specifically, the project will: (1) develop novel predictive dynamic models based on partial differential equations, (2) uncover effects of the interaction between nonlinearity and uncertainty such as noise-induced bifurcations, (3) study infection spikes using a stochastic approach, (4) validate the models using COVID-19 data, (5) establish a control-theoretic framework to analyze mitigative interventions, using a combination of averaging methods from stochastic analysis and feedback control theory, (6) obtain improved characterization of epidemiologic parameters such as basic and effective reproduction numbers, and (7) identify principles and strategies that can inform interventional public health policy.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.

这笔赠款将支持研究，以贡献新的科学知识，这些知识涉及人类行为和致病病原体（例如新型冠状病毒）传播特征的不确定性如何影响流行病的传播，以及由此获得的有关流行病传播的新知识如何有助于干预性公共卫生政策措施，以有效减轻和控制流行病。该研究将通过加强国家为及早有效缓解未来潜在流行病爆发的准备工作，推动预测流行病传播的科学以及国家繁荣。能够准确预测特定时间段内跨地理区域的流行病传播的数学和计算模型是制定有效缓解措施的关键前提，例如社会疏远措施和疫苗接种运动（当疫苗可用时）。然而，现有预测模型的局限性（正如美国 COVID-19 疫情期间所体现的那样）凸显了该领域对新知识的需求。该奖项支持基础研究，以开发新型流行病传播预测模型，并根据目前可用的广泛的 COVID-19 传播数据验证模型预测。这项研究涉及偏微分方程数学理论、随机分析、控制理论和流行病学等多个学科，其结果可能对生态学、气候科学和野火传播等领域的罕见事件动力学研究具有更广泛的意义。此外，这个涉及多个机构的跨学科项目将扩大代表性不足群体对研究和培训的参与，并促进科学和工程教育。该研究将增进人们对人类行为和病原体特征的不确定性如何影响时空、随机流行病动态的基础知识，并产生一个控制理论框架来分析缓解干预措施。具体来说，该项目将：(1) 基于偏微分方程开发新颖的预测动态模型，(2) 揭示非线性和不确定性之间相互作用的影响，例如噪声引起的分岔，(3) 使用随机方法研究感染峰值，(4) 使用 COVID-19 数据验证模型，(5) 建立控制理论框架，使用平均组合来分析缓解干预措施 随机分析和反馈控制理论的方法，(6) 获得流行病学参数的改进特征，例如基本和有效再生数，以及 (7) 确定可为干预性公共卫生政策提供信息的原则和策略。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。

项目成果

Dynamic Instabilities and Pattern Formation in Diffusive Epidemic Spread

流行病扩散的动态不稳定性和模式形成

• DOI: 10.1016/j.ifacol.2023.12.067
• 发表时间: 2023
• 期刊: IFAC-PapersOnLine
• 作者: Singh, Aman Kumar;Miller, Grace;Kumar, Manish;Ramakrishnan, Subramanian
• 通讯作者: Ramakrishnan, Subramanian