时变SIRD模型的柔性滚动回归框架：对COVID-19的应用
A Flexible Rolling Regression Framework for Time-Varying SIRD models: Application to COVID-19
摘要：
本文介绍了一个数据驱动的框架，用于描述COVID-19背景下SIRD模型的时变性质。通过在混合整数双层非线性规划问题中嵌入滚动回归，我们的目标是为研究界提供一个模型，该模型准确地再现了观察到的感染，康复和死亡病例数量的变化，同时提供有关SIRD模型参数的时间依赖性的信息。我们提出了这个优化模型和遗传算法来解决它的解决方案。此外，我们使用明尼苏达州2020年COVID-19数据测试了该算法，发现我们的结果在定性和定量上都是一致的，从而证明所提出的框架是描述大流行动态的有效灵活工具。
英文摘要：
The present paper introduces a data-driven framework for describing the time-varying nature of an SIRD model in the context of COVID-19. By embedding a rolling regression in a mixed integer bilevel nonlinear programming problem, our aim is to provide the research community with a model that reproduces accurately the observed changes in the number of infected, recovered, and death cases, while providing information about the time dependency of the parameters that govern the SIRD model. We propose this optimization model and a genetic algorithm to tackle its solution. Moreover, we test this algorithm with 2020 COVID-19 data from the state of Minnesota and found that our results are consistent both qualitatively and quantitatively, thus proving that the framework proposed is an effective an flexible tool to describe the dynamics of a pandemic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
