ArticlePloS one2012
Comparability of results from pair and classical model formulations for different sexually transmitted infections.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Mathematical Models of Chlamydia Transmission to Address Public Health Policy Questions: A Systematic Review.Sexually transmitted diseases · 2017Pooled it
- Agent based modelling of blood borne viruses: a scoping review.BMC infectious diseases · 2024Article
- A dynamic power-law sexual network model of gonorrhoea outbreaks.PLoS computational biology · 2019Article
- The epidemiological models of Karl-Peter Hadeler.Infectious Disease Modelling · 2018Article
- Pair formation models for sexually transmitted infections: A primer.Infectious Disease Modelling · 2017Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The "classical model" for sexually transmitted infections treats partnerships as instantaneous events summarized by partner change rates, while individual-based and pair models explicitly account for time within partnerships and gaps between partnerships. We compared predictions from the classical and pair models over a range of partnership and gap combinations. While the former predicted similar or marginally higher prevalence at the shortest partnership lengths, the latter predicted self-sustaining transmission for gonorrhoea (GC) and Chlamydia (CT) over much broader partnership and gap combinations. Predictions on the critical level of condom use (C(c)) required to prevent transmission also differed substantially when using the same parameters. When calibrated to give the same disease prevalence as the pair model by adjusting the infectious duration for GC and CT, and by adjusting transmission probabilities for HIV, the classical model then predicted much higher C(c) values for GC and CT, while C(c) predictions for HIV were fairly close. In conclusion, the two approaches give different predictions over potentially important combinations of partnership and gap lengths. Assuming that it is more correct to explicitly model partnerships and gaps, then pair or individual-based models may be needed for GC and CT since model calibration does not resolve the differences.
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