MicroCOSM: a model of social and structural drivers of HIV and interventions to reduce HIV incidence in high-risk populations in South Africa

Abstract

Executive summary Background and objectives South Africa has one of the highest HIV incidence rates in the world. 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. 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. Mathematical models have an important role to play in evaluating the potential impact of new HIV prevention and treatment strategies. 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. 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. 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. Agent-based models (ABMs) overcome many of the limitations of deterministic models, although at the expense of greater computational burden. 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 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. Methods The model is an extension of a previously-published ABM of HIV and other sexually transmitted infections (STIs) in South Africa. This model simulates a representative sample of the South African population, starting from 1985, with an initial sample size of 20 000. The population changes in size as a result of births and deaths. Each individual is assigned a date of birth, sex and race (demographic characteristics). This in turn affects the assignment of socio-economic variables. 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. 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. The model assigns to each individual a number of healthcare access variables that determine their HIV and pregnancy risk. These include their ‘condom preference’ (a measure of the e...

Optimal HIV testing strategies for South Africa: a model-based evaluation of population-level impact and cost-effectiveness

“…This analysis is based on the MicroCOSM model, an agent-based model that was developed to simulate HIV and other STIs in South Africa 31 . A detailed description of the model is provided elsewhere 32 . Briefly, the model simulates a nationally-representative sample of individuals, with the initial simulated population size set to 20 000 at the start of the simulation (in 1985).…”

“…The model simulates eight different HIV testing modalities that have already been introduced in South Africa: 'general' HIV testing (self-initiated testing, testing for insurance purposes and provider-initiated testing not included in other modalities), ANC testing, testing in OI patients, testing partners of newly-diagnosed individuals ('passive referral'), STI patient testing, testing men seeking MMC, testing in prisons and testing in individuals receiving PrEP 32 . Key HIV testing assumptions are summarized in Table 1, and a more detailed description of these 'baseline testing modalities' is provided in the supplementary materials (section 2.1).…”

“…For the purpose of probabilistic sensitivity analysis, we randomly varied several of the model parameters simultaneously. As described elsewhere 32 , 100 different combinations of HIV transmission parameters and sexual behaviour parameters were identified that yielded model estimates of HIV prevalence consistent with historical South African HIV prevalence data. For each of these 100 parameter combinations we randomly sampled 5 parameter combinations from the distributions shown in Table 1, to generate a total of 500 different parameter combinations.…”

Calibration of individual-based models to epidemiological data: A systematic review

“…Several simulation tools (i.e. CEPAC [34], EMOD [35] HIV-CDM [36], MicroCOSM [37], PATH [38], STDSIM [39] and TITAN [40]) were used in the articles modelling HIV. Similarly, two platforms (i.e.…”

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

“…A large amount of general frameworks for individual-based simulations have been developed in the last decades. These platforms vary widely in terms of platform properties, usability, operating ability, pragmatics and security management, making [5][6][7] GEMFsim continuous, Gillespie algorithm static 8 FAVITES continuous, Gillespie algorithm static 9 EpiModel discrete dynamic 10 STDsim discrete dynamic 11 NetLogo discrete dynamic 12,13 EMOD discrete dynamic 14 HIV-CDM discrete dynamic 15 MicroCOSM discrete dynamic 16 Path 2.0 discrete dynamic (through RNetLogo) 17 TITAN discrete dynamic 18 Table 1. Functional and structural differences between SimpactCyan and existing tools for IBM studies in HIV epidemiology.…”