Multi-level statistical modelling has helped support the growth of a movement to address the social determinants of health. Similarly, STRIVE researchers at the London School of Hygiene & Tropical Medicine (LSHTM) will investigate modelling approaches that better reflect how structural factors affect HIV risk.
Current epidemiological modelling of HIV focuses on quantifying the relationship between different risk behaviours and patterns of HIV transmission. This intervention aims to develop mathematical models that account for social factors such as gender relations and demonstrate their influence on the risk behaviours that cause HIV.
As mathematical models have had a central role in informing HIV policy at both national and international level, the purpose of this intervention is to support continued investment in the structural drivers of HIV risk.
- What is the relationship between different structural factors and different indicators of HIV vulnerability?
- Can the influence of these factors be incorporated into mathematical models of HIV transmission to enable the influence of structural interventions to be better quantified?
- What may be the impact of reducing levels of problematic alcohol use on HIV risk behaviour and patterns of HIV transmission in different populations?
- Can data from STRIVE intervention studies be used to help parameterise such models?
Modelling analyses of the potential influence of problematic alcohol use on HIV transmission between sex workers and clients in Karnataka, India
Epidemiological analyses of data sets, ecological analysis, systematic reviews, development of spreadsheet and dynamic HIV transmission models
- Disaggregating 'transactional sex' from 'sex work' - Learning Lab
- Using mathematical modelling to assess HIV prevalence among the “hidden” MSM population
- Stigma as a barrier to the elimination of new infant infections: Model projections from an urban PMTCT programme in South Africa
- Can the UNAIDS Modes of Transmission model be improved?: a comparison of the original and revised model projections using data from a setting in West Africa