Browsing by Subject "Transmission probability"

Now showing 1 - 2 of 2
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    A Stochastic Model for Assessing Chlamydia trachomatis Transmission Risk Using Longitudinal Observational Data
    (Oxford University Press, 2011) Tu, Wanzhu; Ghosh, Pulak; Katz, Barry P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    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.
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    Estimating age-dependent per-encounter chlamydia trachomatis acquisition risk via a Markov-based state-transition model
    (BioMed Central, 2014-04-25) Teng, Yu; Kong, Nan; Tu, Wanzhu; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Chlamydial infection is a common bacterial sexually transmitted infection worldwide, caused by C. 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. trachomatis by adolescent women via unprotected heterosexual acts. Methods: 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. Results: Our case study supports the beliefs that age is an inverse predictor of C. trachomatis transmission and that protective immunity developed after initial infection is only partial. Conclusions: Our modeling method offers a flexible and expandable platform for investigating STI transmission.