A new model for COVID-19 in the post-pandemic era

Xiaoying Pan; Longkun Tang; Xiaoying Pan; Longkun Tang

doi:10.3934/math.20241032

AIMS Mathematics

2024, Volume 9, Issue 8: 21255-21272. doi: 10.3934/math.20241032

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Research article

A new model for COVID-19 in the post-pandemic era

Xiaoying Pan ,
Longkun Tang ^,

School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China

Received: 15 April 2024 Revised: 25 May 2024 Accepted: 06 June 2024 Published: 01 July 2024
MSC : 92B05, 93D05

Coronavirus disease 2019 (COVID-19) in the early days of the pandemic had significant differences in propagation and contact modes from those in the post-pandemic era. In order to capture the real dynamic behavior of COVID-19 propagation in the post-pandemic era, this study takes into account groups with the awareness of self-protection (including taking self-quarantine measures), as well as with loss of immunity, and establishes a new SLEIRS (Susceptible, Low-risk, Asymptomatic infected, Infected and Recovered) epidemic model with births and deaths on the basis of an SEIR model through adding compartment for low-risk groups. For the proposed model, we proved the existence of equilibrium points, identified the stability condition of equilibrium points as well as the basic regeneration number, and verified the proposed theoretical results with numerical simulations. Furthermore, the analysis of the impact of parameters on disease transmission has revealed that detecting the asymptomatic infected is a good measure to prevent and control the disease transmission. More practically, we used the particle swarm optimization (PSO) algorithm to estimate the model parameters based on the real epidemic data, and we then applied the model with estimated parameters to make predictions for the half-a-month epidemic in 2022. Results show the prediction and the estimated parameters are basically consistent with the practical situation, indicating that the proposed model has good capability in short-term prediction for COVID-19 in the post-pandemic.
- epidemic models,
- COVID-19,
- nonlinear dynamics,
- parameter estimation
Citation: Xiaoying Pan, Longkun Tang. A new model for COVID-19 in the post-pandemic era[J]. AIMS Mathematics, 2024, 9(8): 21255-21272. doi: 10.3934/math.20241032

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Abstract

Coronavirus disease 2019 (COVID-19) in the early days of the pandemic had significant differences in propagation and contact modes from those in the post-pandemic era. In order to capture the real dynamic behavior of COVID-19 propagation in the post-pandemic era, this study takes into account groups with the awareness of self-protection (including taking self-quarantine measures), as well as with loss of immunity, and establishes a new SLEIRS (Susceptible, Low-risk, Asymptomatic infected, Infected and Recovered) epidemic model with births and deaths on the basis of an SEIR model through adding compartment for low-risk groups. For the proposed model, we proved the existence of equilibrium points, identified the stability condition of equilibrium points as well as the basic regeneration number, and verified the proposed theoretical results with numerical simulations. Furthermore, the analysis of the impact of parameters on disease transmission has revealed that detecting the asymptomatic infected is a good measure to prevent and control the disease transmission. More practically, we used the particle swarm optimization (PSO) algorithm to estimate the model parameters based on the real epidemic data, and we then applied the model with estimated parameters to make predictions for the half-a-month epidemic in 2022. Results show the prediction and the estimated parameters are basically consistent with the practical situation, indicating that the proposed model has good capability in short-term prediction for COVID-19 in the post-pandemic.

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