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

这笔赠款将支持以下研究：人类行为和致病病原体(如新冠肺炎中的新型冠状病毒)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。这项研究将促进预测流行病传播的科学，并通过加强国家对及早和有效缓解未来潜在流行病暴发的准备来促进国家繁荣。能够准确预测在特定时间段内跨地理区域传播的流行病的数学和计算模型是制定有效的缓解干预措施的关键先导，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--就像美国新冠肺炎疫情期间所表现的那样--突显出在这一领域需要新的知识。该奖项支持基础研究，以开发新的流行病传播预测模型，并根据目前仅有的广泛的新冠肺炎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，这个跨学科项目是一个涉及多个机构的合作项目，将扩大未被充分代表的群体在研究和培训中的参与，并推动科学和工程教育。研究将促进关于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础知识，并产生一个控制理论框架来分析缓解措施。具体地说，该项目将：(1)开发基于偏微分方程的新的预测动态模型，(2)揭示非线性和不确定性之间相互作用的影响，例如噪声诱导的分支，(3)使用随机方法研究感染高峰，(4)使用新冠肺炎数据验证模型，(5)建立控制理论框架，使用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)获得诸如基本和有效繁殖次数等流行病学参数的改进表征，以及(7)确定可以为干预性公共卫生政策提供信息的原则和战略。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.