Different models of sexually transmitted infections (STIs) can yield substantially different conclusions about STI epidemiology, and it is important to understand how and why models differ. Frequency-dependent models make the simplifying assumption that STI incidence is proportional to STI prevalence in the population, whereas network models calculate STI incidence more realistically by classifying individuals according to their partners' STI status.
We assessed a deterministic frequency-dependent model approximation to a microsimulation network model of STIs in South Africa. Sexual behavior and demographic parameters were identical in the 2 models. Six STIs were simulated using each model: HIV, herpes, syphilis, gonorrhea, chlamydia, and trichomoniasis.
For all 6 STIs, the frequency-dependent model estimated a higher STI prevalence than the network model, with the difference between the 2 models being relatively large for the curable STIs. When the 2 models were fitted to the same STI prevalence data, the best-fitting parameters differed substantially between models, with the frequency-dependent model suggesting more immunity and lower transmission probabilities. The fitted frequency-dependent model estimated that the effects of a hypothetical elimination of concurrent partnerships and a reduction in commercial sex were both smaller than estimated by the fitted network model, whereas the latter model estimated a smaller impact of a reduction in unprotected sex in spousal relationships.
The frequency-dependent assumption is problematic when modeling short-term STIs. Frequency-dependent models tend to underestimate the importance of high-risk groups in sustaining STI epidemics, while overestimating the importance of long-term partnerships and low-risk groups.
We assessed a deterministic frequency-dependent model approximation to a microsimulation network model of sexually transmitted infections. The frequency-dependent approximation failed to produce consistent results, especially in the case of short-term sexually transmitted infections. Supplemental digital content is available in the text.
From the Centres for *Infectious Disease Epidemiology and Research and †Social Science Research, University of Cape Town, Cape Town, South Africa
Acknowledgments: The authors thank Jeff Eaton for providing helpful comments on an earlier draft of this manuscript. They also thank Haroon Moolla for assistance in updating the systematic review of South African STI prevalence data (see Supplemental Digital Content 2, http://links.lww.com/OLQ/A127).
Funded, in part, by the South African National AIDS Council, with seed funding from UNAIDS. No conflicts of interest are declared.
This work was presented, in part, at the 2013 STI and AIDS World Congress, Vienna, Austria (abstract P3.227).
Correspondence: Leigh F. Johnson, PhD, Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Anzio Road, Observatory 7925, Cape Town, South Africa. E-mail: Leigh.Johnson@uct.ac.za.
Received for publication September 3, 2015, and accepted December 3, 2015.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (http://www.stdjournal.com).