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Dynamic Modeling of Antibody Level Changes with Individual Age: The Case of COVID-19 Spread in China

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Abstract

COVID-19 is a ribonucleic acid virus with a high mutation frequency and will continue to mutate as time goes on. At the same time, the number of antibodies in the human body will gradually decrease with time. This paper analyzed the dissemination mechanism of COVID-19 in China after implementing the liberalization policy, established a waning immunity model with age heterogeneity, and explored the threshold dynamics of the system and the dynamic behavior of equilibrium. According to the latest release of COVID-19 data from China, the model parameters were fitted and analyzed using the least squares method to verify the model. We also predicted that the peak of the next epidemic in China would come around June 27. Furthermore, we used numerical simulations to research the impact of different infection rates on disease and compare them with the absence of waning immunity. Finally, using the cost-effectiveness analysis method, we determined the best strategy based on the number of deaths and medical resources. The results show that when \({{\beta }_{11}}>0.27\), controlling the transmission rate between people under 65 is the optimal solution; however, when \({{\beta }_{11}}<0.27\), we should pay more attention to the infected people aged 65 and above.

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Funding

This research is partially supported by National Natural Science Foundation of China under Grant 12271314 and Graduate Innovation Project of Shanxi Province 2022Y587.

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Li Li & Yangqing Wang

  2. Data Science and Technology, North University of China, Taiyuan, 030051, China

    Wei Zhang

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  1. Li Li
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  2. Yangqing Wang
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  3. Wei Zhang
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Correspondence to Li Li.

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Li, L., Wang, Y. & Zhang, W. Dynamic Modeling of Antibody Level Changes with Individual Age: The Case of COVID-19 Spread in China. Int. J. Appl. Comput. Math 9, 83 (2023). https://doi.org/10.1007/s40819-023-01592-6

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