Abstract

This study aims to develop an advanced mathematic model and investigate when and how will the COVID-19 in the US be evolved to endemic. We employed a nonlinear ordinary differential equations-based model to simulate COVID-19 transmission dynamics, factoring in vaccination efforts. Multi-stability analysis was performed on daily new infection data from January 12, 2021 to December 12, 2022 across 50 states in the US. Key indices such as eigenvalues and the basic reproduction number were utilized to evaluate stability and investigate how the pandemic COVD-19 will evolve to endemic in the US. The transmissional, recovery, vaccination rates, vaccination effectiveness, eigenvalues and reproduction numbers (R0 and R0end) in the endemic equilibrium point were estimated. The stability attractor regions for these parameters were identified and ranked. Our multi-stability analysis revealed that while the endemic equilibrium points in the 50 states remain unstable, there is a significant trend towards stable endemicity in the US. The study's stability analysis, coupled with observed epidemiological waves in the US, suggested that the COVID-19 pandemic may not conclude with the virus's eradication. Nevertheless, the virus is gradually becoming endemic. Effectively strategizing vaccine distribution is pivotal for this transition.

Details

Title
Equilibrium points and their stability of COVID-19 in US
Author
Hu, Xiaoxi 1 ; Hu, Zixin 2 ; Xu, Tao 3 ; Zhang, Kai 4 ; Lu, Henry H. 5 ; Zhao, Jinying 3 ; Boerwinkle, Eric 6 ; Jin, Li 1 ; Xiong, Momiao 7 

 Fudan University, State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443) 
 Fudan University, State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Fudan University, Artificial Intelligence Innovation and Incubation Institute, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443) 
 University of Florida, Department of Epidemiology, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091) 
 University at Albany, State University of New York, Department of Environmental Health Sciences, School of Public Health, Rensselaer, USA (GRID:grid.265850.c) (ISNI:0000 0001 2151 7947) 
 BeiGene, Cambridge, USA (GRID:grid.519096.2) 
 The University of Texas Health Science Center at Houston, School of Public Health, Houston, USA (GRID:grid.267308.8) (ISNI:0000 0000 9206 2401) 
 The University of Texas Health Science Center at Houston, Department of Biostatistics and Data Science, School of Public Health, Houston, USA (GRID:grid.267308.8) (ISNI:0000 0000 9206 2401) 
Pages
1628
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2916279732
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.