Abstract
Sexually transmitted infections (STIs) are a globally increasing public health problem. Mathematical models, carefully matched to available epidemiological and behavioural data, have an important role to play in predicting the action of control measures. Here, we explore the effect of concurrent sexual partnerships on the control of a generic STI with susceptible-infected-susceptible dynamics. Concurrency refers to being in more than one sexual partnership at the same time, and is difficult to measure accurately. We assess the impact of concurrency through the development of three nested pair-formation models: one where infection can only be transmitted via stable sexual partnerships, one where infection can also be transmitted via casual partnerships between single individuals, and one where those individuals in stable partnerships can also acquire infection from casual partnerships. For each model, we include the action of vaccination before sexual debut to inform about the ability to control. As expected, for a fixed transmission rate, concurrency increases both the endemic prevalence of infection and critical level of vaccination required to eliminate the disease significantly. However, when the transmission rate is scaled to maintain a fixed endemic prevalence across models, concurrency has a far smaller impact upon the critical level of vaccination required. Further, when we also constrain the models to have a fixed number of new partnerships over time (both long-term and casual), then increasing concurrency can slightly decrease the critical level of vaccination. These results highlight that accurate measures and models of concurrency may not always be needed for reliable forecasts when models are closely matched to prevalence data. We find that, while increases in concurrency within a population are likely to generate public-health problems, the inclusion of concurrency may be unnecessary when constructing models to determine the efficacy of the control of STIs by vaccination.
Highlights
Controlling the spread of sexually transmitted infections (STIs) remains an important public health challenge globally
Despite this generic approach, it is still important that we use parameters that reflect the general behavioural dynamics of human populations and the general epidemiology that is comparable with STIs
We acknowledge that our simplified model cannot capture the complex heterogeneities of the true sexual network; for example, the rates of partnership, break-ups, and concurrent partnerships are not fixed, but rather are culturally situated social conventions (Adimora and Schoenbach, 2005), which change over time (Haavio-Mannila, 2001)
Summary
Controlling the spread of sexually transmitted infections (STIs) remains an important public health challenge globally. We use the models developed to explore the effect of concurrency on the transmission of an STI and on the critical level of vaccination required to eliminate the infection from the population We perform this analysis in two distinct scenarios: firstly, when the epidemiological and behavioural parameters are fixed and the level of concurrency is allowed to vary, mimicking changing patterns of sexual behaviour; and secondly, when models with and without concurrency are matched to available data, capturing the impact of model misspecification. Xiridou et al (2003, 2004) model concurrency in a similar approach to this paper to assess the contribution of stable and casual partnerships to the spread of HIV While all these models highlight the implications of concurrency on transmission and endemic prevalence of infection, to our knowledge, the implications that concurrency has on the control of STIs when parameters are matched to data has not been fully explored. We use this generic formulation to observe the effects of concurrency on transmission and control, and to inform future researchers whether modelling concurrent partnerships explicitly is necessary in more sophisticated models of STI control
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