HIV treatment in an individual-based model



The human immunodeficiency virus (HIV) has a devastating effect on human health. In order to control the spread of this virus, it is imperative to implement preventive strategies. Modelling HIV epidemics allows researchers to evaluate and validate preventive strategies. One way to model such epidemics is by means of individual-based simulation.

HIV infections typically progress over a period of many years. To model such an epidemic, it is necessary to properly capture the progress of the infection within a patient. Each patient exhibits a unique disease progression that is subject to the natural variation of the virus and the patient’s immune system. Additionally, the treatment that patients undergo has an important impact on their disease progression and the patient's infectiousness. However, since HIV is a fast evolving virus, resistance towards such treatments can occur, resulting in a decrease or absence in therapy effectivity (i.e. therapy failure).

The figure demonstrates a simplified agent-based model that captures individuals and their sexual relationships in the context of an HIV epidemic. The model deals with several properties of the individual (therapy, preventive measures, ...). However, we also need to model the disease progression of each infected individual. This research project will allow us to incorporate individual therapeutic trajectories in our individual-based model.

Research question

In a previous research study, we were able to infer trends related to therapy failure from a Portuguese clinical database. Various options for modeling these trends were explored. You will further validate whether such trends can consistently be inferred from other European datasources, and select a model appropriate to your findings. You will implement this model in our indvidual-based simulation framework, and report its impact on the simulation outcomes.

Research context

This research project offers challenges in the context of computational biology and computer science. When your research efforts are successful, the results will be incorporated into our epidemiological models. Therefore, your research will contribute to the state-of-the art of HIV modelling and assist in the development and validation of new preventive strategies.

We believe that, when this research project is executed successfully, the results will be suitable to be presented in a peer-reviewed conference or journal.


Prior knowledge in the field of machine learning is required (e.g. the course of Machine Learning or Statistical Machine Learning). The project does not require any prior knowledge related to epidemiology or biological processes, an interest in this matter is highly appreciated.


prof. dr. Ann Nowé 


Contact Pieter Libin for more information -- by e-mail ( or visit me at 10.G.730A.