Cost-effectiveness evaluation of gender-based vaccination programs against sexually transmitted infections

The ultimate goal of a vaccination program is to interrupt pathogen transmission so as to eradicate the disease from the population in the future, and/or to decrease morbidity and mortality due to the disease in the short term. For sexually transmitted infections (STI) the determination of an optimal vaccination program is not straightforward since (1) the transmission probabilities between two different sexes are normally unequal (weighted to a greater probability from males to females than vice versa), (2) demographic parameters between the two sexes are unequal, (3) the prevalence of disease in one sex may have a greater impact on the morbidity and mortality of the next generation (transmission to the neonate) and, (4) the existence of pathogens closely related to the STI in question (i.e. herpes - HSV-1 vs. HSV-2, different strains of Chlamydia trachomatis, different strains of Neisseria which cause Gonorrhea, and others) may induce immunity in individuals that render a vaccine ineffective. We have developed two models of sexually transmitted infections (with and without age structure) to evaluate the cost-efficacy of gender-based vaccination programs in the context of STI control. The first model ignores age structure for qualitative analysis of points (1-3), while the second refined one incorporates the age structure, reecting the effects of immunity gained from infection of closely related strains (point 4), which is important for HSV-2 vaccination strategies. For both models, we find that the stability of the system and ultimate eradication of the disease depends explicitly on the corresponding reproduction number. We also find that vaccinating females is more cost-effective, providing a greater reduction in disease prevalence in the population and number of infected females of childbearing age. This result is counter-intuitive since vaccinating super-transmitters (males) over sub-transmitters (females) usually has the greatest impact on disease prevalence. Sensitivity analysis is implemented to investigate how the parameters affect the control reproduction numbers and infectious population sizes.