Mathematical insights into the influence of interventions on sexually transmitted diseases

We establish a mathematical model to analyze what factors cause the epidemics of sexually transmitted diseases (STDs) and how to eliminate or mitigate them. According to the level of prevention awareness, we divide the susceptible population into two groups of individuals, whose behavior, population size, and recruitment rate are affected by the interventions. First, the threshold, R0, of STDs model is obtained. If R0 < 1, the disease-free equilibrium is globally asymptotically stable. We also obtain the conditions for switching the equilibrium state of the model among disease-free equilibrium, endemic equilibrium, and limit cycle. Second, the threshold and transcritical bifurcation show that interventions for high-risk sexual behaviors of high-risk susceptible individuals can eliminate STDs. Additionally, sex education, influenced by the size of infected individuals and by interventions, can effectively cut down the size of STDs. Third, extending the survival time of the infected individual may prolong the time to end STDs unless they reject high-risk sexual behavior. Fourth, we analyze the existence, stability, and direction of Hopf bifurcation, which may explain the periodic oscillation in the size of infected population.

Automated summary

We propose a mathematical model to examine the causes of sexually transmitted diseases (STDs) epidemics and potential mitigation strategies. By categorizing the susceptible population based on prevention awareness, we analyze how their behavior, population size, and recruitment rate are affected by interventions. Our findings suggest that interventions targeting high-risk susceptible individuals can eliminate STDs, and sex education can effectively reduce the overall infection size. Furthermore, extending the survival time of infected individuals may delay the control of STDs unless high-risk sexual behavior is rejected. We also explore the presence, stability, and direction of Hopf bifurcation, which may explain the periodic oscillations in the infected population size.