There has been a huge shift in the HIV prevention field, in which biomedical interventions have been elevated to the forefront of research and practice. This has re-energized efforts to expand global access to antiretroviral therapy and support treatment as prevention (TasP), as well as to expand targeted access to pre-exposure prophylaxis (PrEP). While there is enthusiasm about the prospect of ending HIV, it is critical to consider how our intervention efforts will interact with the complex demographic and biological systems in which they are placed. Our project brings together experienced social and biomedical scientists for a unique interdisciplinary project: to build an integrated methodological framework to study the multi-level biological and behavioral foundations of the population dynamics of HIV. We will combine newly developed stochastic models for representing the dynamics of human partnership networks with models that represent viral evolutionary dynamics within and between hosts. The result will be a comprehensive framework for studying the impact of biomedical interventions on HIV evolution and drug resistance under different transmission network conditions.
Our specific aims are: 1) To create a new public software package that integrates models for viral transmission networks and intra- and inter-host viral dynamics and evolution;2) To determine how transmission network structure and biomedical interventions influence trends in population-level measures of HIV viral load, including set point viral load (as a proxy for HIV virulence) and community viral load (as a potential metric for intervention impact evaluation);and 3) To investigate the interaction of transmission network structure and biomedical interventions on the emergence and spread of HIV drug resistance mutations.
We will create a novel methodological framework that integrates the social dynamics of sexual networks with the evolutionary dynamics of HIV. This unique integrative framework will lead to critical insights on HIV epidemiology and prevention, and provide new tools for a wide range of basic research areas, including virology, evolutionary biology, epidemiology, and network analysis.
|Herbeck, Joshua T; Mittler, John E; Gottlieb, Geoffrey S et al. (2014) An HIV epidemic model based on viral load dynamics: value in assessing empirical trends in HIV virulence and community viral load. PLoS Comput Biol 10:e1003673|