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Abstract

There are complex interactions between antibody levels and epidemic propagation; the antibody level of an individual influences the probability of infection, and the spread of the virus influences the antibody level of each individual. There exist some viruses that, in their natural state, cause antibody levels in an infected individual to gradually decay. When these antibody levels decay to a certain point, the individual can be reinfected, such as with COVID-19. To describe their interaction, we introduce a novel mathematical model that incorporates the presence of an antibody retention rate to investigate the infection patterns of individuals who survive multiple infections. The model is composed of a system of stochastic differential equations (SDE) to derive the equilibrium point and threshold of the model and presents rich experimental results of numerical simulations to further elucidate the propagation properties of the model. We find that the antibody decay rate strongly affects the propagation process, and also that different network structures have different sensitivities to the antibody decay rate, and that changes in the antibody decay rate cause stronger changes in the propagation process in Barabási–Albert (BA) networks. Furthermore, we investigate the stationary distribution of the number of infection states and the final antibody levels, and find that they both satisfy the normal distribution, but the standard deviation is small in the Barabási–Albert (BA) network. Finally, we explore the effect of individual antibody differences and decay rates on the final population antibody levels, and uncover that individual antibody differences do not affect the final mean antibody levels. The study offers valuable insights for epidemic prevention and control in practical applications.