Data science training needs in sub-Saharan Africa: Implications for biomedical research and therapeutics capacity

Abstract

Data use is becoming increasingly valuable worldwide and has positively improved decision-making in various sectors. Data science has revolutionized the study of epidemiology and disease control. Despite the unlimited potential of data science, Africa lags in its innovation and technological advancements. Although sub-Saharan Africa (SSA) has not taken full advantage of data science in healthcare delivery and biomedical research, attempts have been made to harness this opportunity. This article reviews the current level, potentials, and gaps in data science, genomics, and bioinformatics in SSA. We also identified needs associated with developing tools and building capacity in the region, while also acknowledging advances made in these areas to date. We identified opportunities in health data science in SSA, including benefits in combating diseases that burden this region. Applying data science has the potential to create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care, reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life in general. In order to achieve this, artificial intelligence and machine learning to build and apply models are some of the common mechanisms applied in all domains of medical research, alongside computational tools and models to simulate systems or expedite biomedical research like bioinformatics and computational biology. Data science, a body of knowledge that uses modern tools and techniques for data management and utilization of big data for innovative interventions and biomedical research is upcoming in Sub-Saharan Africa. Its importance in understanding epidemiology of diseases, public health, and surveillance as well as other domains in allied medical disciplines were highlighted in this review. The dearth in data science skills, expertise, and institutions engaged in training data science needs urgent intervention to optimize the potential uses to improve medicine and healthcare delivery in SSA.