ArticleNonlinear dynamics2023
Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.
Article in Nonlinear dynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 25 citations in OpenAlex.
- Optimal strategies for fair vaccine allocation: integrating non-pharmaceutical interventions, regional quarantine, and social challenges in epidemic control.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy.Infectious Disease Modelling · 2024Article
- Exploring the dynamics of monkeypox transmission with data-driven methods and a deterministic model.Frontiers in epidemiology · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.
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