Despite the existence of effective methods for prevention, HIV continues to be a global health crisis. The need for an HIV vaccine remains paramount. The phase III RV144 vaccine trial is the only trial of an HIV vaccine that showed modest success in preventing infection, with an estimated 31% vaccine efficacy at 3.5 years post- enrollment. However, vaccines can result in the emergence and spread of vaccine-resistant strains, via natural selection and strain replacement. The potential for such population-level adaptation in HIV has not been considered in HIV vaccine-related modeling studies. Our recent work in HIV evolutionary and epidemic modeling suggests that HIV may adapt rapidly in response to a partially effective vaccine similar to RV144. Our goal for this project is to predict the potential population-level impact of an HIV evolutionary response to vaccination. First, we will extend our existing HIV epidemic model (EvoNetHIV) to incorporate a broad set of vaccine-related parameters. Second, we will use this model to quantify outcomes of viral adaptation and predict public health impact across the set of vaccine-related parameters. We expect that incorporating a vaccine-specific evolutionary framework into our HIV epidemic model will substantially improve predictions related to the public health impact of HIV vaccination programs. We also expect that, due to our open source software philosophy, our evolutionary approach will be easily integrated into the epidemic models of other scientific groups.
The potential for HIV adaptation in response to vaccination has, to date, not been systematically evaluated. In this project, we will create a novel modeling framework that integrates HIV evolutionary processes into mathematical models of HIV epidemiology and vaccination. This approach will lead to more accurate predictions of vaccine impact, improved vaccine design, and ultimately greater public health impact for vaccination programs.
