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

该基金将支持能够提供新科学知识的研究，这些科学知识涉及人类行为和致病病原体（如COVID-19中的新型冠状病毒）的传播特征的不确定性如何影响流行病的传播，以及由此获得的关于流行病传播的新知识如何有助于有效缓解和控制流行病的干预性公共卫生政策措施。这项研究将通过加强国家对未来可能爆发的流行病的早期有效缓解的准备，促进流行病传播的科学预测和国家繁荣。能够准确预测流行病在特定时期内跨地理区域传播的数学和计算模型是制定有效的缓解干预措施（如保持社交距离措施和疫苗接种运动（当有疫苗时））的关键前提。然而，现有预测模型的局限性，正如在美国COVID-19爆发期间所显示的那样，强调了这一领域对新知识的需求。该奖项支持基础研究，以开发新的流行病传播预测模型，并根据目前仅有的大量COVID-19传播数据验证模型预测。本研究涉及偏微分方程数学理论、随机分析、控制理论、流行病学等多个学科，研究结果将在生态学、气候科学、野火传播等领域的罕见事件动力学研究中具有更广泛的意义。此外，这个涉及多个机构的跨学科合作项目将扩大代表性不足的群体对研究和培训的参与，并促进科学和工程教育。这项研究将促进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力学的基本认识，并产生一个控制理论框架来分析缓解干预措施。具体而言，该项目将：(1)基于偏微分方程建立新的预测动态模型；(2)揭示非线性和不确定性（如噪声引起的分岔）之间相互作用的影响；(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，结合随机分析和反馈控制理论的平均方法来分析缓解干预措施。(6)获得流行病学参数的改进特征，如基本和有效繁殖数；(7)确定可以为干预性公共卫生政策提供信息的原则和策略。该奖项反映了美国国家科学基金会的法定使命，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。

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.