Molecular epidemiology to aid virtual elimination of HIV transmission in Australia - Billal M. Obeng; Anthony D. Kelleher; Francesca Di Giallonardo (Virus Research, 2024)
The Global UNAIDS 95/95/95 targets aim to increase the percentage of persons who know their HIV status, receive antiretroviral therapy, and have achieved viral suppression. Achieving these targets requires efforts to improve the public health response to increase access to care for those living with HIV, identify those yet undiagnosed with HIV early, and increase access to prevention for those most at risk of HIV acquisition. HIV infections in Australia are among the lowest globally having recorded significant declines in new diagnoses in the last decade. However, the HIV epidemic has changed with an increasing proportion of newly diagnosed infections among those born outside Australia observed in the last five years. Thus, the current prevention efforts are not enough to achieve the UNAIDS targets and virtual elimination across all population groups. We believe both are possible by including molecular epidemiology in the public health response. Molecular epidemiology methods have been crucial in the field of HIV prevention, particularly in demonstrating the efficacy of treatment as prevention. Cluster detection using molecular epidemiology can provide opportunities for the real-time detection of new outbreaks before they grow, and cluster detection programs are now part of the public health response in the USA and Canada.Here, we review what molecular epidemiology has taught us about HIV evolution and spread. We summarize how we can use this knowledge to improve public health measures by presenting case studies from the USA and Canada. We discuss the successes and challenges of current public health programs in Australia, and how we could use cluster detection as an add-on to identify gaps in current prevention measures easier and respond quicker to growing clusters. Lastly, we raise important ethical and legal challenges that need to be addressed when HIV genotypic data is used in combination with personal data.