Knowledge Resource
Research Summary: Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 27 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
A bibliometric analysis of Scopus-indexed literature reveals a significant disparity between global and African artificial intelligence research addressing climate-related health outcomes. While international efforts prioritize advanced predictive modeling for planetary health, African initiatives remain confined to localized, reactive triage for infectious diseases due to critical infrastructure and data constraints.
Why it matters
This disparity highlights an operational mismatch between climate vulnerability and technological capacity in public health management. Without foundational infrastructure, regional health systems cannot shift from reactive treatment to proactive, predictive risk mitigation. Addressing these constraints is essential to prevent systemic public health failures as climate pressures intensify.
Key insights
- African public health is disproportionately affected by climate change, yet artificial intelligence predictive modeling applications remain severely constrained.
- Bibliometric network analysis indicates global research prioritizes complex predictive modeling for planetary health, whereas African studies concentrate on reactive, localized infectious disease triage.
- Scaling advanced artificial intelligence across the region is hindered by structural barriers, specifically compute deficits, data silos, and energy instability.
- International funding models predominantly target isolated pilot initiatives rather than sustainable, foundational infrastructure.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2610.35294
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- Verification ID
- ASA-EXE-2026-01454
- Version
- v1.0 · r0
- Issued
- 27 October 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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