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

该资助将支持与人类行为和病原体（如COVID-19的新型冠状病毒）传播特征的不确定性如何影响流行病传播有关的新科学知识，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公共卫生政策措施，以有效缓解和控制流行病。这项研究将通过加强国家对早期和有效缓解未来潜在流行病爆发的准备，促进预测流行病传播的科学和国家繁荣。数学和计算模型可以准确地预测在特定时间段内在地理区域传播的流行病，这是制定有效干预措施的重要前提，例如社交距离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（如美国COVID-19疫情期间所显示的）凸显了对该领域新知识的需求。该奖项支持基础研究，以开发新的流行病传播预测模型，并根据目前可用的大量COVID-19传播数据验证模型预测。这项研究涉及多个学科，包括偏微分方程的数学理论，随机分析，控制理论和流行病学，其结果可能在生态学，气候科学和野火传播等领域的稀有事件动力学研究中具有更广泛的意义。此外，这一跨学科项目是多个机构的合作努力，将扩大代表性不足的群体在研究和培训中的参与，并促进科学和工程教育。该研究将推进人类行为和病原体特征的不确定性如何影响时空的基础知识，随机流行动力学，并产生一个控制理论框架，分析干预措施，以减轻。具体而言，该项目将：（1）开发基于偏微分方程的新型预测动态模型，（2）揭示非线性和不确定性之间相互作用的影响，如噪声引起的分叉，（3）使用随机方法研究感染高峰，（4）使用COVID-19数据验证模型，（5）建立控制理论框架以分析缓解干预措施，使用来自随机分析和反馈控制理论的平均方法的组合，（6）获得流行病学参数的改进表征，例如基本和有效繁殖数，（七）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。

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.