Application of dynamic mode decomposition and compatible window-wise dynamic mode decomposition in deciphering COVID-19 dynamics of India

Abstract

The COVID-19 pandemic recently caused a huge impact on India, not only in terms of health but also in terms of economy. Understanding the spatio-temporal patterns of the disease spread is crucial for controlling the outbreak. In this study, we apply the compatible window-wise dynamic mode decomposition (CwDMD) and dynamic mode decomposition (DMD) techniques to the COVID-19 data of India to model the spatial-temporal patterns of the epidemic. We preprocess the COVID-19 data into weekly time-series at the state-level and apply both the CwDMD and DMD methods to decompose the data into a set of spatial-temporal modes. We identify the key modes that capture the dominant features of the COVID-19 spread in India and analyze their phase, magnitude, and frequency relationships to extract the temporal and spatial patterns. By incorporating rank truncation in each window, we have achieved greater control over the system’s output, leading to better results. Our results reveal that the COVID-19 outbreak in India is driven by a complex interplay of regional, demographic, and environmental factors. We identify several key modes that capture the patterns of disease spread in different regions and over time, including seasonal fluctuations, demographic trends, and localized outbreaks. Overall, our study provides valuable insights into the patterns of the COVID-19 outbreak in India using both CwDMD and DMD methods. These findings can help public health organizations to develop more effective strategies for controlling the spread of the pandemic. The CwDMD and DMD methods can be applied to other countries to identify the unique drivers of the outbreak and develop effective control strategies.

Keywords

• dimensionality reduction
• disease modeling
• infectious disease
• rank truncation
• 37m10
• 37n99
• 65p99