The 2015 Sustainable Development Goals (SDGs) require countries to commit to ending preventable maternal, newborn and child deaths by 2030. These goals include reducing under-five mortality in each country to at least 25 per 1000 live births, at least 12 per 1,000 live births for neonatal mortality, and globally at least 70 per 100,000 live births for maternal mortality (1, 2). There needs to be high-quality data on all pertinent indicators in order for nations to be held responsible for their promises (3). Even though countries have made significant work towards achieving these targets, progress is still sluggish, particularly when it comes to birth and deaths registration, and survival and well-being of the most vulnerable (4). UNICEF monitors child well-being through 48 indicators (5) for example, birth registration; education; protection from harm; clean and safe surroundings; and poverty eradication in life, but data gaps remain a significant challenge in many countries (6)

Similarly, over 190 countries worldwide offer data to different United Nations agencies on various indicators like mortality, stillbirth, morbidity, preterm birth, and low birthweight, among other variables (7). However, it is remarkable that the world’s regions with the highest burdens, like Africa, faces a Data Rich Information Poor (DRIP) syndrome(8-10). Accurate data on maternal newborn and child health (MNCH) morbidity and mortality are essential for holding leadership in African countries accountable and achieving the goals of reducing preventable deaths and improved wellbeing (11). In addition to accountability, accurate data would guide better-targeted interventions for the most vulnerable to obtain the most significant impact despite limited resources. Countries need to set priority agendas for MNCH, there is a critical need for data-driven approaches to identify and prioritize key areas. (12-18). During the recent Coronavirus pandemic, there were new advancements using data science in tracking and reporting global mortality such as rapid mortality surveillance and epidemic response within the World Health Organisation’s (WHO)’s Toolkit for Routine Health Information Systems Data (19). However, the pandemic also significantly disrupted MNCH indicators tracking, as well as essential services like immunisation and broader health care provision, including widespread school closures (5, 20-23).

Data science, an interdisciplinary field amalgamating scientific methodologies, algorithms, and systems, is instrumental in deriving insights and knowledge from structured and unstructured data (24). It synthesises elements from mathematics, statistics, computer science, and specific domain knowledge such as MNCH to facilitate data engineering, organisation and management, analysis, and interpretation and dissemination of large volumes of data for informed decision-making (25). Over the last two decades, data science has been applied in several sectors, including healthcare, finance, and marketing, to solve complex challenges, forecast outcomes, and inform strategic approaches (26). Data science applications have also enabled tracking and progress of several SDG targets with improvements in interoperability and improved routine information systems (27, 28). In MNCH, especially in Africa, various applications have been documented, for example predicting infant mortality in Rwanda using ML (29), Prediction of poliovirus, vaccine coverage and immunisation programs in Africa (30, 31) and using data science approaches for decision-making and utilisation of maternal healthcare services in rural Ethiopia (32, 33). With data science applications, there is potential to integrate African MNCH data with other planetary datasets like temperatures, seasonality, rainfall, air pollution, flooding, and NASA’s space data (24).

Data science initiatives elsewhere (Europe, the US, and Asian settings) have been found useful in improving data quality, care and service provision, planning and priority setting as well as implementing targeted interventions for MNCH for example digitisation of health information systems (34-37). Utilising data science in MNCH, mortality and morbidity tracking, data collection, and reporting has the potential to improve the quality, comprehensiveness, and utilisation of data in Africa. Fortunately, many efforts to harness data science in Africa are underway such as District Health Information Software 2 (DHIS2), digital payments, mobile money, ehealth and mhealth platforms (38). However, many of these efforts act in isolation and are not very well coordinated and are not implemented as part of national efforts that can be used to guide policy decisions.

In this study, we conduct a scoping review that aims to review the use, role and contribution of data science to improved MNCH in Africa in recent years. Specifically, the study aims to provide a landscape of data science in MNCH by mapping where, how, and for what MNCH outcomes data science approaches have been applied to in the African region.