Can the prevalence of one STI serve as a predictor for another? A mathematical modeling analysis

Abstract

We aimed to understand to what extent knowledge of the prevalence of one sexually transmitted infection (STI) can predict the prevalence of another STI, with application for men who have sex with men (MSM). An individual-based simulation model was used to study the concurrent transmission of HIV, HSV-2, chlamydia, gonorrhea, and syphilis in MSM sexual networks. Using the model outputs, 15 multiple linear regression models were conducted for each STI prevalence, treating the prevalence of each as the dependent variable and the prevalences of up to four other STIs as independent variables in various combinations. For HIV, HSV-2, chlamydia, gonorrhea, and syphilis, the proportion of variation in prevalence explained by the 15 models ranged from 34.2% to 88.3%, 19.5%–70.5%, 43.7%–82.9%, 48.7%–86.3%, and 19.5%–67.2%, respectively. Including multiple STI prevalences as independent variables enhanced the models' predictive power. Gonorrhea prevalence was a strong predictor of HIV prevalence, while HSV-2 and syphilis prevalences were weak predictors of each other. Propagation of STIs in sexual networks reveals intricate dynamics, displaying varied epidemiological profiles while also demonstrating how the shared mode of transmission creates ecological associations that facilitate predictive relationships between STI prevalences.

Keywords

• Sexually transmitted infection
• Sexually transmitted disease
• Men who have sex with men
• Mathematical modeling
• Epidemiology
• Public health