Combining phylodynamics and agent-based HIV transmission modelling to advance epidemiological methodology and evidence based public health policies for HIV prevention and treatment

October 2015 to September 2018
HIV prevention
HIV transmission
epidemiological methodology
Hasselt University
University of Leuven (KU Leuven)
South African Centre for Epidemiological Modelling and Analysis (SACEMA) (South Africa)
ETH Zurich (Switzerland)
Research fields
Medicine and Health Sciences

High rates of HIV transmission remain an important public health challenge. HIV Phylodynamics is the study of HIV viral genetic data to  understand  the HIV transmission dynamics behind an HIV epidemic. Agent-based HIV  transmission  models  simulate  how HIV epidemics evolve, based on estimates and assumptions of infectious disease parameters. Both methodologies could be combined synergistically,  but this area of epidemiological methodology is underinvestigated. The proposed research aims to address this research gap, based on the hypothesis that such a combined approach can lead to a stronger evidence-base for policy making in HIV prevention and treatment.
This new project will focus on the development of new methodology and software implementation for integrating HIV phylodynamics and agent- based HIV transmission models, followed by a series of simulation experiments that demonstrate the added value of the new methodology. Although the focus of this project is the epidemiology of HIV infections among MSM in Switzerland, the new methodological framework has many more potential future applications, which may be explored in subsequent projects. These include estimation of the prevention impact of earlier access to HIV treatment, and monitoring the rate of acquired and transmitted HIV drug resistance in other European countries and South Africa.
In 2016, we published an extensive, narrative review of existing data and models for sexual network inference in HIV epidemiology. This review made clear that data on sexual networks come from an increasingly diverse array of sources, but that each of these sources only document parts of the networks through which HIV may spread. Egocentric network surveys suffer from non-response, social desirability bias and the inability to probe beyond the immediate network connections of individuals. Through partner notification services, realised and potential HIV transmission pathways may be partially revealed, but in resource-poor settings with generalised HIV epidemics offering this may require prohibitively large investments. Phylogenetic tree analysis permits reconstructing parts of the HIV transmission chains by linking genetically related infections, but to be informative, HIV sequence data must be available for what may be an unfeasibly  large sample of PLWH. Novel methods to combine these data sources are beginning to  emerge  from the collaborative efforts of experts in computational biology, social science, statistics, public health and epidemiological modelling. Further advances in network  analysis  for  HIV  epidemiology  will require (1) important methodological developments in network modelling, as well  as  (2)  a  long-term, global commitment from researchers and funding agencies to ensure open access to analytical tools and multifaceted network datasets that include HIV sequences along with behavioural, demographic, clinical and programmatic information.