A Comparison of Two Mathematical Modeling Frameworks for Evaluating Sexually Transmitted Infection Epidemiology

• Published in: Sexually transmitted diseases 2016-03, Vol.43 (3), p.139-146
• Main Authors: Johnson, Leigh F., Geffen, Nathan
• Format: Article
• Language: English
• Subjects: Chlamydia trachomatis; Computer simulation; Demographics; Elimination; Epidemics; Epidemiology; Female; Fitting; Frequency dependence; Gonorrhea; High risk; HIV; Human immunodeficiency virus; Humans; Immunity; Incidence; Infections; Male; Mathematical models; Models, Statistical; Original Study; Parameters; Partnerships; Prevalence; Prostitution; Reduction; Risk groups; Risk Reduction Behavior; Safe sexual practices; Sex; Sexual behavior; Sexual Behavior - statistics & numerical data; Sexual Partners; Sexually transmitted diseases; Sexually Transmitted Diseases - epidemiology; Sexually Transmitted Diseases - transmission; Short term; Simulation; STD; Syphilis; Trichomoniasis
• ISSN: 0148-5717; 1537-4521
• DOI: 10.1097/OLQ.0000000000000412

BACKGROUNDDifferent 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 partnersʼ STI status. METHODSWe 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 modelHIV, herpes, syphilis, gonorrhea, chlamydia, and trichomoniasis. RESULTSFor 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. CONCLUSIONSThe 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.