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Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities

Jean Marie Tshimula, Mitterrand Kalengayi, Dieumerci Makenga, Dorcas Lilonge, Marius Asumani, Déborah Madiya, Élie Nkuba Kalonji, Hugues Kanda, René Manassé Galekwa, Josias Kumbu, Hardy Mikese, Grace Tshimula, Jean Tshibangu Muabila, Christian N. Mayemba, D'Jeff K. Nkashama, Kalonji Kalala, Steve Ataky, Tighana Wenge Basele, Mbuyi Mukendi Didier, Selain K. Kasereka, Maximilien V. Dialufuma, Godwill Ilunga Wa Kumwita, Lionel Muyuku, Jean-Paul Kimpesa, Dominique Muteba, Aaron Aruna Abedi, Lambert Mukendi Ntobo, Gloria M. Bundutidi, Désiré Kulimba Mashinda, Emmanuel Kabengele Mpinga, Nathanaël M. Kasoro

TL;DR

This paper surveys how artificial intelligence can strengthen public health surveillance in Africa by enhancing disease detection, prediction, and real-time reporting across major infectious diseases (HIV, cholera, Ebola, measles, TB) and mental health. It highlights diverse data sources (clinical records, environmental data, genomic data, social media) and AI methods (ML/DL, CAD, agent-based models, GIS, and AVADAR) that improve accuracy and timeliness, enabling targeted interventions and optimized resource use. The analysis covers opportunities (infrastructure bridging, local empowerment, community engagement, and LLMs) and challenges (ethics, capacity, equity, and electricity), offering concrete recommendations for implementation. Collectively, the findings demonstrate AI's potential to transform surveillance systems in low-resource African contexts, supporting proactive public health actions and stronger health outcomes. The paper emphasizes the need for context-specific data integration, capacity-building, and governance to realize these benefits safely and equitably.

Abstract

Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.

Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities

TL;DR

This paper surveys how artificial intelligence can strengthen public health surveillance in Africa by enhancing disease detection, prediction, and real-time reporting across major infectious diseases (HIV, cholera, Ebola, measles, TB) and mental health. It highlights diverse data sources (clinical records, environmental data, genomic data, social media) and AI methods (ML/DL, CAD, agent-based models, GIS, and AVADAR) that improve accuracy and timeliness, enabling targeted interventions and optimized resource use. The analysis covers opportunities (infrastructure bridging, local empowerment, community engagement, and LLMs) and challenges (ethics, capacity, equity, and electricity), offering concrete recommendations for implementation. Collectively, the findings demonstrate AI's potential to transform surveillance systems in low-resource African contexts, supporting proactive public health actions and stronger health outcomes. The paper emphasizes the need for context-specific data integration, capacity-building, and governance to realize these benefits safely and equitably.

Abstract

Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.
Paper Structure (28 sections, 4 figures, 2 tables)

This paper contains 28 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Applications of AI in public health surveillance
  3. Disease detection and prediction
  4. HIV detection and prediction
  5. Cholera outbreaks
  6. Ebola prediction
  7. Measles surveillance
  8. Tuberculosis detection
  9. Real-time surveillance and reporting
  10. Influenza surveillance
  11. Zika virus monitoring
  12. COVID-19 tracking
  13. Malaria reporting
  14. Poliovirus surveillance
  15. Mental health monitoring
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Taxonomy of AI applications for public health.
  • Figure 2: Applications of AI in public health surveillance discussed in this paper.
  • Figure 3: Performance metrics of best models per disease for Disease prediction and detection. Note that y-axis indicates Disease + Authors + Model + Metric.
  • Figure 4: Performance metrics of best models per disease for Real-time surveillance and reporting. Note that y-axis indicates Disease + Authors + Model + Metric.