the genotype test, which detects mutations in the viral genetic code. This information is then typically interpreted using rules equating individual mutations with resistance against individual drugs. However, there are many different interpretation systems available that do not always agree, the systems only provide categorical results (resistant, sensitive or intermediate) and they do not necessarily relate well to how a patient will respond to a combination of drugs in the clinic. RDI Overview The RDI was established in 2002 to pioneer a new approach: to develop computational models using a wide range of laboratory and clinical data collected from thousands of patients treated with HAART all over the world and to use these models to predict how an individual patient will respond to different combinations of drugs. Key to the success of this approach is the collection of large amounts of data with which to train the models and the use of data from as wide and heterogeneous range of sources as possible to maximise the generalisability of the models’ predictions. In order to achieve this, the RDI set out to involve as many clinics worldwide as possible and to be the single repository for the data required, in an attempt to avoid unnecessary duplication of effort and competition. As of January 2019, the RDI has collected data from approximately 250,000 patients from dozens of clinics in more than 30 countries. It is probably the largest database of its kind in the world. The data includes demographic information for the patient, and multiple determinations of the amount of virus in the patient's bloodstream, CD4 cells counts (a white blood cell critical to the function of the immune system that HIV targets and destroys), genetic code of the patients virus, and details of the drugs that have been used to treat the patient. The RDI has used these data to conduct extensive research in order to develop the most accurate system possible for the prediction of treatment response. This research involved the development and comparison of different computational modelling methods including artificial neural networks, support vector machines, random forests and logistical regression. The predictions of the RDI's models have historically correlated well with the actual changes in virus load of patients in the clinic, typically achieving a correlation co-efficient of 0.7 or more. Recent models have predicted whether a combination treatment will reduce the level of virus in the patient's bloodstream