A Continuum Deformation Approach for Growth Analysis of COVID-19 in the United States
Abstract The COVID-19 global pandemic has significantly impacted every aspect of life all over the world. The United States is reported to have suffered more than 20% of the global casualties from this pandemic. It is imperative to investigate the growth dynamics of the disease in the US based on varying geographical and governmental factors that best manifest itself in each state of the country. This paper utilizes a hybrid machine learning and continuum deformation-based approach for analyzing the stability of the rapid COVID-19 growth. The proposed continuum deformation model is used to learn the parameters of pandemic growth based on the training data of total cases, deaths, and recoveries in each state of the United States from March 12, 2020 to January 28, 2021. Using this approach, multiple periods of the nationwide and State-level pandemic growth patterns are discovered and analyzed.