Contrasting views on the infectivity of gonorrhoea.

Felton, W F

doi:10.1136/sti.49.2.151

Cited by 1 publication

(1 citation statement)

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“…Using the estimated prevalence of Neisseria gonorrhoeae among the FSWs and the reported number of sexual contacts between the crew and the FSWs, the investigators could estimate the female-to-male Neisseria gonorrhoeae transmission probability. Despite concerns about the accuracy of the Neisseria gonorrhoeae prevalence estimate for the FSWs due to the limited sampling frame and potential bias associated with unregistered FSWs (Felton, 1973;Hooper et al, 1978;Baumgarten, 1980), the estimate from this experiment remains the only accepted measure of the gonorrhoea transmission probability. Studies such as this, however, are no longer recognized as ethical.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model for AssessingChlamydia TrachomatisTransmission Risk by Using Longitudinal Observational Data

Ghosh

Katz

2011

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary 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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Section: Introductionmentioning

confidence: 99%