Mustavee Shakib, Agarwal Shaurya, Enyioha Chinwendu, Das Suddhasattwa
Department of Civil Engineering, University of Central Florida, Orlando, FL 32816 USA.
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816 USA.
Nonlinear Dyn. 2022;109(2):1233-1252. doi: 10.1007/s11071-022-07469-5. Epub 2022 May 5.
This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and the input-output characteristics as a locally linear controlled dynamical system. The approach solely relies on the snapshots of spatiotemporal data and does not require any knowledge of the system's underlying physical laws. We exploit the Koopman operator framework by utilizing the Hankel dynamic mode decomposition with Control (HDMDc) algorithm to obtain a linear disease spread model incorporating human mobility as a control input. The study demonstrated that the proposed methodology could capture the impact of local mobility on the early dynamics of the ongoing global pandemic. The obtained locally linear model can accurately forecast the number of new infections for various prediction windows ranging from two to four weeks. The study corroborates a leader-follower relationship between mobility and disease spread dynamics. In addition, the effect of delay embedding in the HDMDc algorithm is also investigated and reported. A case study was performed using COVID infection data from Florida, US, and HAM data extracted from
本文研究了人类活动与流动性（HAM）在传染病传播动态中的影响。具体而言，它探讨了HAM及其对COVID-19病毒早期传播的影响之间的相互联系。在疫情早期阶段，有效繁殖数与人类流动模式呈现出高度相关性，从而引出了一个假设，即HAM系统可作为一个与疾病传播动态耦合的系统进行研究。本研究应用带有控制输入的广义库普曼框架来确定非线性疾病传播动态以及作为局部线性受控动态系统的输入-输出特性。该方法仅依赖于时空数据的快照，无需任何关于系统潜在物理规律的知识。我们通过利用带控制的汉克尔动态模式分解（HDMDc）算法来利用库普曼算子框架，以获得一个将人类流动作为控制输入纳入的线性疾病传播模型。研究表明，所提出的方法能够捕捉局部流动性对当前全球大流行早期动态的影响。所获得的局部线性模型能够准确预测从两周到四周不等的各种预测窗口内的新增感染病例数。该研究证实了流动性与疾病传播动态之间的主从关系。此外，还研究并报告了HDMDc算法中延迟嵌入的影响。使用来自美国佛罗里达州的COVID感染数据以及从……提取的HAM数据进行了案例研究。
