A Physics-Based Data-Driven Approach for Finite Time Estimation of Pandemic Growth

COVID-19 is a global health crisis that has had unprecedented, widespread impact on households across the United States and has been declared a global pandemic on March 11, 2020 by World Health Organization (WHO) [1]. According to Centers for Disease Control and Prevention (CDC) [2], the spread of COVID-19 occurs through person-to-person transmission i.e. close contact with infected people through contaminated surfaces and respiratory fluids carrying infectious virus. This paper presents a data-driven physics-based approach to analyze and predict the rapid growth and spread dynamics of the pandemic. Temporal and Spatial conservation laws are used to model the evolution of the COVID-19 pandemic. We integrate quadratic programming and neural networks to learn the parameters and estimate the pandemic growth. The proposed prediction model is validated through finite time estimation of the pandemic growth using the total number of cases, deaths and recoveries in the United States recorded from March 12, 2020 until October 1, 2021 [3].