Introduction HIV and herpes simplex virus type 2 (HSV-2) infections are sexually transmitted and propagate in sexual networks. Our study’s objectives were to quantify effects of key network statistics on infection transmission, and extent to which HSV-2 prevalence is predictive of HIV prevalence.
Methods An individual-based Monte Carlo simulation model was constructed to describe sex partnering and infections transmission. The model was parametrized with current and representative natural history, transmission, and sexual behaviour data. Correlations were assessed on model outcomes and multiple linear regressions were conducted to estimate adjusted associations and effect sizes.
Results HIV prevalence was most often lower than a third of HSV-2 prevalence. HIV and HSV- 2 prevalences were associated with a Spearman’s rank correlation coefficient of 0.64 (95% CI: 0.58–0.69). Collinearities among network statistics were detected, most notably between concurrency versus mean and variance of number of partners. Controlling for confounding, unmarried mean number of partners (or alternatively concurrency) were the strongest predictors of HIV prevalence. Meanwhile, unmarried and married mean and variance of number of partners (or alternatively concurrency), and clustering coefficient were the strongest predictors of HSV-2 prevalence. HSV-2 prevalence was a strong predictor of HIV prevalence by proxying effects of network statistics.
Conclusion Network statistics drive similar and differential effects on HIV and HSV-2 transmission. HIV prevalence reflected primarily mean and variance of number of partners, but HSV-2 prevalence was affected by a range of network statistics. HSV-2 prevalence can be used to predict a population’s HIV epidemic potential, thereby informing HIV prevention interventions.