Chlamydial infection is a common bacterial sexually transmitted infection worldwide, caused byC. trachomatis. The screening for C. trachomatis has been proven to be successful. However, such success is not fully realized through tailoring the recommended screening strategies for different age groups. This is partly due to the knowledge gap in understanding how the infection is correlated with age. In this paper, we estimate age-dependent risks of acquiring C. trachomatisby adolescent women via unprotected heterosexual acts.


We develop a time-varying Markov state-transition model and compute the incidences of chlamydial infection at discrete age points by simulating the state-transition model with candidate per-encounter acquisition risks and sampled numbers of unit-time unprotected coital events at different age points. We solve an optimization problem to identify the age-dependent estimates that offer the closest matches to the observed infection incidences. We also investigate the impact of antimicrobial treatment effectiveness on the parameter estimates and the differences between the acquisition risks for the first-time infections and repeated infections.


Our case study supports the beliefs that age is an inverse predictor of C. trachomatistransmission and that protective immunity developed after initial infection is only partial.


Our modeling method offers a flexible and expandable platform for investigating STI transmission.


This is the publisher pdf of Yu Teng, Nan Kong, Wanzhu Tu. Estimating Age-Dependent Per-Encounter Chlamydia Trachomatis Acquisition Risk Via a Markov-Based State-Transition Model. Journal of Clinical Bioinformatics 2014, 4:7 and is available at: 10.1186/2043-9113-4-7.

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