Probabilities of condom use also depend on demographic characteristics and relationship type, and are assumed to be strongly associated with levels of educational attainment. Frequencies of sex are assumed to depend on demographic characteristics and relationship type, and migrant couples are assumed to have reduced coital frequency. Individuals marry their short-term partners at rates that depend on their demographic characteristics. Each time a new short-term partner is acquired, the individual is linked to another individual in the population, with the probability of linkage depending on the individual’s sexual preference and preference for individuals of the relevant age, risk group, race, location and educational attainment. ![]() Individuals are assumed to enter into short-term relationships at rates that depend on their risk group and demographic characteristics. Three types of relationship are modelled: sex worker-client contacts, short-term (non-marital) relationships and long-term (marital or cohabiting) relationships. Each individual is also assigned a sexual preference, which can change over their life course. Sexual behaviour is simulated by assigning to each individual an indicator of their propensity for concurrent partnerships (‘high risk’ individuals are defined as individuals who have a propensity for concurrent partnerships or commercial sex). Access to these healthcare services changes over time, and is also assumed to depend on demographic and socioeconomic variables, as well as on the individual’s health status. These include their ‘condom preference’ (a measure of the extent to which they wish to use condoms and are able to access condoms), use of hormonal contraception and sterilization, use of pre-exposure prophylaxis (PrEP), male circumcision, HIV testing history and uptake of antiretroviral treatment (ART). The model assigns to each individual a number of healthcare access variables that determine their HIV and pregnancy risk. Each individual is also assigned to an urban or rural location, with rates of movement between urban and rural areas depending on demographic characteristics and educational attainment. Each individual is assigned a level of educational attainment, which is dynamically updated as youth progress through school and tertiary education, with rates of progression and drop-out depending on the individual’s demographic characteristics. This in turn affects the assignment of socio-economic variables. Each individual is assigned a date of birth, sex and race (demographic characteristics). The population changes in size as a result of births and deaths. This model simulates a representative sample of the South African population, starting from 1985, with an initial sample size of 20 000. Methods The model is an extension of a previously-published ABM of HIV and other sexually transmitted infections (STIs) in South Africa. The objective of this report is to provide a technical description of the model and to explain how the model has been calibrated to South African data sources future publications will assess the drivers of HIV transmission in South Africa in more detail. This study presents an ABM of HIV in South Africa, developed to characterize the key social drivers of HIV in South Africa and the groups that are at the highest risk of HIV. ![]() The frequency-dependent assumption implicit in most deterministic models also leads to under-estimation of the contribution of high-risk groups to the incidence of HIV.Īgent-based models (ABMs) overcome many of the limitations of deterministic models, although at the expense of greater computational burden. ![]() In addition, many of the mathematical models that have been developed are relatively simple deterministic models, which are not well suited to simulating the complex causal pathways that link many of the social drivers to HIV incidence. However, most of the mathematical modelling studies that have been published to date have evaluated biomedical HIV prevention strategies, and relatively few models have been developed to understand the role of social determinants or interventions that address these social drivers. Mathematical models have an important role to play in evaluating the potential impact of new HIV prevention and treatment strategies. Understanding the social determinants of HIV is closely related to understanding high-risk populations (‘key populations’), since many of the factors that place these key populations at high HIV risk are social and behavioural rather than biological. Although much research has focused on developing biomedical strategies to reduce HIV incidence, there has been less investment in prevention strategies that address the social drivers of HIV spread. Background and objectives South Africa has one of the highest HIV incidence rates in the world.
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