Improving drug-resource allocation in combating HIV
To provide an estimation for Drug resistance mutational group using bayesian inference which can help country to allocate resources on priority drug distributions
Local actors who are trying to combat HIV in Kenya, face a chronic lack of information regarding the prevalence of the different virological types of HIV in their region. Not knowing what viral strain is most prevalent in each in the different regions of the country, severely weakens the ability of those actors to allocate medicine in the most effective manner and this way adequately respond to the medicinal needs of the local populations.
To get a real-life feeling for this dilemma, dive into the fictive story of, Valu, a resource manager for the WHO, in charge of allocating the right medicine and the right amount of medicine to particular regions in Kenya. The problem of Valu is that because he does not know what mutation of HIV is most prevalent in his county, he has to send bulk amounts of all different types of medicine to the different centres, without being able to estimate how much of what particular medicine is actually needed.
However, Valu, has heard of a pilot project called 4Kenya and asks them for help. 4Kenya have developed a Baysian model of statistical inference, which enables them to accurately estimate the prevalence of different viral strains in the county Valu is responsible for. This in turn empowers Valu, to provide medicine based on the needs of the local population and thus ensure that „medicinal supply meets the virological demand“.
To sum up, the model developed by 4Kenya, enables actors that have to allocate medicine in order to combat HIV, to improve their decision making strategies and ensure that the dispatching of medicines corresponds to the prevalence of virological strains in the given region.