A Stochastic Model for Assessing Chlamydia trachomatis Transmission Risk Using Longitudinal Observational Data

Bacterium Chlamydia trachomatis causes genital chlamydia infection. Yet little is known about the transmission efficiency of this organism. Ethical constraint against exposing healthy subjects to infected partners precludes the possibility of quantifying transmission risk through controlled experiments. This research proposes an alternative strategy that relies on observational data. Specifically, we present a stochastic model that treats longitudinally observed infection states in a group of young women as a Markov process. The proposed model explicitly accommodates the parameters of C. trachomatis transmission, including per-encounter sexually transmitted infection (STI) acquisition risks, with and without condom protection, and the probability of antibiotic treatment failure. The male-to-female transmission probability of C. trachomatis is then estimated by combining the per-encounter disease acquisition risk and the organism's prevalence in the male partner population. The proposed model is fitted in a Bayesian computational framework.