title: A new computer model for estimating the impact of vaccination protocols and its application to the study of Chlamydia trachomatis genital infections.
abstract: We describe a new computer model for studying the impact of vaccination protocols on the prevalence of disease. This state transition model uses two parameters, the forward rate and the cure rate, that are derived from epidemiological data to compute the percentage of individuals infected in the population. The cure rate is the percentage of individuals that are cured within one time step (which in this study is one year) and the forward rate is used to calculate the number of new infections due to transmission. The forward rate and the cure rate are incorporated into an update function that has the property that if the vaccination efficacy is 0%, or no vaccination is applied, then the percentage of individuals infected stays constant. We present computer simulations of this model designed to assess the influence of two variables on the prevalence of Chlamydia trachomatis infection in a study population. More specifically, we determined the effect of vaccines with efficacies ranging from 50% to 100% and we analysed the impact on the population for vaccines efficacious for periods of 10, 20 and 40 years. The results of the computer simulation show that even the least efficacious vaccination programme rapidly decreases the prevalence of C. trachomatis infection in the population. On the other hand, a vaccine that is efficacious for a period of only 1 year had minimal impact on the prevalence of the disease in the total population.
authors.affiliation: Numinous Noetics Group, Massachusetts Institute of Technology, Cambridge 02139-3563, USA.
authors.forename: M A
authors.initials: MA
authors.lastname: de la Maza
authors.forename: L M
authors.initials: LM
authors.lastname: de la Maza
mesh_terms: D000293:Adolescent; D000328:Adult; D002690:Chlamydia Infections / Q000453:epidemiology / Q000517:prevention & control*; D002692:Chlamydia trachomatis / Q000276:immunology*; D003198:Computer Simulation; D005260:Female; D005831:Genital Diseases, Female / Q000453:epidemiology / Q000517:prevention & control*; D005832:Genital Diseases, Male / Q000453:epidemiology / Q000517:prevention & control*; D006801:Humans; D008297:Male; D008875:Middle Aged; D018448:Models, Immunological; D011247:Pregnancy; D011251:Pregnancy Complications, Infectious / Q000453:epidemiology / Q000517:prevention & control; D015995:Prevalence