ABSTRACT
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). According to Centers for Disease Control and Prevention (CDC), 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. © 2022 Elsevier B.V.. All rights reserved.
ABSTRACT
The COVID-19 global pandemic has significantly impacted people throughout the United States and the World. While it was initially believed the virus was transmitted from animal to human, person-to-person transmission is now recognized as the main source of community spread. This article integrates data into physics-based models to analyze stability of the rapid COVID-19 growth and to obtain a data-driven model for spread dynamics among the human population. The proposed mass-conservation model is used to learn the parameters of pandemic growth and to predict the growth of total cases, deaths, and recoveries over a finite future time horizon. The proposed finite-time prediction model is validated by finite-time estimation of the total numbers of infected cases, deaths, and recoveries in the United States from March 12, 2020 to December 9, 2020. IEEE