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Partnership dynamics in mathematical models and implications for representation of sexually transmitted infections: a review.

ABSTRACT: Mathematical models of sexually transmitted disease (STI) are increasingly relied on to inform policy, practice, and resource allocation. Because STI transmission requires sexual contact between two or more people, a model's ability to represent the dynamics of sexual partnerships can influence the validity of findings. This ability is to a large extent constrained by the model type, as different modeling frameworks vary in their capability to capture patterns of sexual contact at individual, partnership, and network levels. In this paper, we classify models into three groups: compartmental, individual-based, and statistical network models. For each framework, we describe the basic model structure and discuss key aspects of sexual partnership dynamics: how and with whom partnerships are formed, partnership duration and dissolution, and temporal overlap in partnerships (concurrency). We illustrate the potential implications of accurately accounting for partnership dynamics, but these effects depend on characteristics of both the population and pathogen; the combined impact of these partnership and epidemiologic dynamics can be difficult to predict. While each of the reviewed model frameworks may be appropriate to inform certain research or policy questions, modelers and consumers of models should carefully consider the implications of sexual partnership dynamics for the questions under study.

SUBMITTER: Rao DW

PROVIDER: S-EPMC8259505 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Sexual mixing between heterogeneous population subgroups is an integral component of mathematical models of sexually transmitted infections (STIs). This study compares the fit of different mixing representations to survey data and the impact of different mixing assumptions on the predicted benefits of hypothetical human papillomavirus (HPV) vaccine strategies. METHODS:We compared novel empirical (data-driven) age mixing structures with the more commonly-used assortative-proportionate (A-P) mixing structure. The A-P mixing structure assumes that a proportion of sexual contacts - known as the assortativity constant, typically estimated from survey data or calibrated - occur exclusively within one's own age group and the remainder mixes proportionately among all age groups. The empirical age mixing structure was estimated from the National Survey on Sexual Attitudes and Lifestyles 3 (Natsal-3) using regression methods, and the assortativity constant was estimated from Natsal-3 as well. Using a simplified HPV transmission model under each mixing assumption, we calibrated the model to British HPV16 prevalence data, then estimated the reduction in steady-state prevalence and the number of infections averted due to expanding HPV vaccination from 12- through 26-year-old females alone to 12-year-old males or 27- to 39-year-old females. RESULTS:Empirical mixing provided a better fit to the Natsal-3 data than the best-fitting A-P structure. Using the model with empirical mixing as a reference, the model using the A-P structure often under- or over-estimated the benefits of vaccination, in one case overestimating by 2-fold the number of infections prevented due to extended female catch-up in a high vaccine uptake setting. CONCLUSIONS:An empirical mixing structure more accurately represents sexual mixing survey data, and using the less accurate, yet commonly-used A-P structure has a notable effect on estimates of HPV vaccination benefits. This underscores the need for mixing structures that are less dependent on unverified assumptions and are directly informed by sexual behavior data.

Project description:BACKGROUND: Sex-specific differences regarding the transmissibility and the course of infection are the rule rather than the exception in the epidemiology of sexually transmitted infections (STIs). Human papillomavirus (HPV) provides an example: disease outcomes differ between men and women, as does the potential for transmission to the opposite sex. HPV vaccination of preadolescent girls was recently introduced in many countries, and inclusion of boys in the vaccination programs is being discussed. Here, we address the question of whether vaccinating females only, males only, or both sexes is the most effective strategy to reduce the population prevalence of an STI like HPV. METHODS AND FINDINGS: We use a range of two-sex transmission models with varying detail to identify general criteria for allocating a prophylactic vaccine between both sexes. The most effective reduction in the population prevalence of infection is always achieved by single-sex vaccination; vaccinating the sex with the highest prevaccine prevalence is the preferred strategy in most circumstances. Exceptions arise only when the higher prevaccine prevalence is due to a substantially lower rate of natural immunity, or when natural immunity is lifelong, and a prolonged duration of infectiousness coincides with increased transmissibility. Predictions from simple models were confirmed in simulations based on an elaborate HPV transmission model. Our analysis suggests that relatively inefficient genital transmission from males to females might render male vaccination more effective in reducing overall infection levels. However, most existing HPV vaccination programs have achieved sufficient coverage to continue with female-only vaccination. CONCLUSIONS: Increasing vaccine uptake among preadolescent girls is more effective in reducing HPV infection than including boys in existing vaccination programs. As a rule, directing prophylactic immunization at the sex with the highest prevaccine prevalence results in the largest reduction of the population prevalence.

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Partnership dynamics in mathematical models and implications for representation of sexually transmitted infections: a review.

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