CAREER: Learning, Estimation, and Control of Networked Epidemic Processes

点击翻译按钮获取中文摘要

Current approaches for controlling diseases spreading through populations employ techniques that rely solely on mathematical models or only depend on data, with no clear connection between these two extremes. The proposed research will establish a set of fundamental theories, tools, and algorithms to model, learn, and control real-time epidemic spreading processes by leveraging multiple live data streams while evaluating the trade-off between model-based and data-driven approaches. The proposed research consists of three thrusts. The first thrust will encompass the design of a set of novel models to characterize epidemic spreading under different settings, providing tools for connecting and comparing models at different resolutions. The next thrust will include algorithm design and development aimed at selecting the appropriate models and identification of model features by leveraging multiple live data streams. The last thrust will incorporate the multi-resolution models from the first two thrusts within a data-driven predictive control framework, studying model-based vs. data-driven approaches. This research will help provide insights for decision makers, such as politicians, public health officials, administrators, and business leaders, to better mitigate future disease outbreaks. The proposed research will develop a class of multi-resolution models that enable nonlinear control design that spans and adapts along the model-based vs. data-driven spectrum and is focused on the application of networked epidemic processes. The project will identify fundamental bounds on achievable performance in the presence of corrupted data sets and provide theories and algorithms with performance guarantees. The proposed research will lead to a greater understanding of the fundamental factors that affect the modeling, learning, and control of networked systems with multiple online data streams; establishing systematic procedures for model selection, estimating parameters and structure from uncertain and biased data; and developing realizable real-time control strategies for and across different model resolutions. The research plan will be integrated into education through a software platform to serve as an educational tool, giving students hands-on experience with data, exponential growth, dynamic modeling, and control.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.