A recent Nature Communications study discusses the importance of knowledge science for health research that might significantly profit the African population.

Study: The promise of knowledge science for health research in Africa. Image Credit: NicoElNino / Shutterstock.com

How has data science revolutionized scientific research?

The applying of knowledge science is related to the processing of massive datasets using high-performance computational infrastructure. These datasets are derived from public, personal, and industrial domains.

Data science has significantly helped research in various fields by aiding in the event of multiple novel interventions and methods which have considerable social advantages. 

Data scientists process an enormous amount of knowledge retrieved from healthcare systems, shopping records, smartphones, social media postings, and wearable devices using novel algorithms. Data evaluation helps generate recent insights and generalizable knowledge.

Evaluation of enormous datasets has positively helped in bio-preparedness, monitoring, and formulation of response strategies to combat infectious disease outbreaks for each plants and animals. For instance, the Geographical Information System (GIS) data is used to map spatial variations in accordance with the incidence, prevalence, and outcomes of diseases. This data can be used to evaluate the effectiveness of the strategy implemented by healthcare systems post-disease outbreak.

Data science has also been used to cut back fraud and corruption, improve supply chain management to forestall a shortage of products, and detect fake pharmaceutical products. In health research, data science is involved with the systematic collection, generation, storage, processing, management, visualization, analyses, interpretation, and communication of health-related data. This evaluation provides actionable insights to forestall or manage a disease outbreak.

Data science in health research in Africa

The implementation of knowledge science in healthcare research would likely solve many challenges faced by the people of Africa. Although Africa constitutes about 17% of the world’s population, it bears 25% of the world’s disease burden.

To further complicate matters, Africa lacks an appropriate variety of healthcare staff and infrastructure. These social and clinical adversities could be attributed to the dearth of properly trained personnel, economic and social instability, and poor funding.

Data scientists have highlighted that African countries require modern data science tools and methods to beat challenges linked to differential climate and disease manifestations from other parts of the world.

Currently, there stays a big gap within the dataset that adequately represents the African population. The underrepresented dataset results in the event of unstable and inaccurate models and algorithms to research the African population.

Academic institutions, governments, African researchers, and the general public sector actively use data science for discoveries, research, and formulation of strategies to administer infection outbreaks. Notably, a lot of these data science tools used have been developed and validated outside Africa.

Tips on how to improve data science health research in Africa

More infrastructure, training programs, scientific conferences, and international collaborations are needed to help within the generation of high-quality datasets, which is able to help develop stable and accurate data science models. These initiatives will effectively close the gap in data science between Africa and high-income countries (HIC).

In 2022, 20 grants price $74.5 million USD were awarded within the Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa) program. The most important objective of this program is to enhance data science and health research in Africa.

Prior to the DS-I Africa program, the Human Heredity & Health in Africa (H3Africa) program enabled the establishment of recent scientific collaborations to develop genomics research infrastructure. This helped expand the African genomics research ecosystem.

Despite the implementation of strict health research ethics infrastructure by African governments and institutions, many challenges regarding the standard of informed consent, benefit-sharing, autonomy, data sharing, privacy, and exploitation prevail. These unresolved challenges and controversies should be addressed to learn data science health research.

It’s imperative to construct and maintain national and institutional health research ethics infrastructure in Africa. Information will support the economic and mental capital of institutions and countries.

Research ethicists must bring local research and data science communities together to evolve data science health research strategies. Well-designed, properly funded, and comparatively long training programs are crucial for data science health research ethics.

Multilateral agencies, including the United Nations, governments, researchers, advocates, bioethicists, and other stakeholders, must work with African institutions to develop guidelines for data science health research to optimize advantages for the worldwide population. 

Data science technologies have been related to the production of algorithmic bias by replicating and reinforcing societal biases. Algorithmic biases, combined with the poor equity and variety within the foundational datasets, cause algorithmic deprivation, distortion, and discrimination. Issues related to data colonization and extractive research should be addressed to cut back biases.

In the long run, novel strategies for data science health research should be developed with targeted objectives, which might significantly profit different health research ecosystems in Africa.

Journal reference:

• Adebamowo, C. A., Callier, S., Akintola, S., et al. (2023) The promise of knowledge science for health research in Africa. Nature Communications 14(1);1-8. doi:10.1038/s41467-023-41809-2