AI Modelling in One Health Infectious Disease Studies in Sub-Saharan Africa: A Review

62 peer-reviewed studies form the basis of this systematic review on AI adoption for infectious disease research in Sub-Saharan Africa. The review, authored by Bruno Enagnon Lokonon and last updated in April 2026, analyzes trends from 2019 to 2024, highlighting dominant diseases and persistent gaps in One Health integration.

Use Cases

• Benchmarking AI model performance based on reported median accuracy metrics for disease classification tasks.
• Identifying research gaps in zoonotic disease modelling based on the underrepresentation of animal and environmental data.
• Analyzing geographic disparities in AI research leadership based on the concentration of studies in South Africa, Ethiopia, Kenya, and Tanzania.
• Reviewing the evolution of AI techniques for disease prediction based on the reported surge in publications from 2021 to 2024.

Strengths

• Based on a systematic review of 62 peer-reviewed studies, providing a synthesized evidence base.
• Identifies specific disease focus areas, with Malaria targeted in 24% of the reviewed studies.
• Explicitly discusses constraints like limited computational infrastructure and data-governance weaknesses.

Limitations

• The dataset is a 15.3 KB DOCX document, indicating a limited scope of structured data.
• Row count and column-level documentation are absent; the content is a review article.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Systematic review of peer-reviewed literature.

Time Range

Studies reviewed up to 2024, with a noted surge from 2021.

Freshness

Last updated 2026-04-22 05:46:47; freshness should be verified

Geography

Sub-Saharan Africa, with specific mention of South Africa, Ethiopia, Kenya, and Tanzania.