Disease outbreaks in Africa strain fragile health systems and expose gaps in preparedness. Institutions across the continent are beginning to use AI to track diseases, predict outbreaks, and strengthen emergency responses.
There are three areas where African countries can make real gains by investing in context-specific AI, covering early warning systems, predictive disease modelling, and targeted public health interventions.
The continent reports more than 160 disease outbreaks every year, yet early detection remains a challenge. The Africa Centres for Disease Control and Prevention (Africa CDC), together with the Africa Pathogen Genomics Initiative, is scaling up genomic surveillance to better identify and track pathogens. Integrating AI into these systems can further improve early detection. Global tools like BlueDot and HealthMap have shown how AI can scan news sources, social media, and public reports to detect unusual disease patterns days before traditional alerts.
Emerging local adaptations and approaches
The Cambridge-Africa Health Data Initiative is working with government institutions and ministries in Nigeria and Kenya to integrate AI into national surveillance infrastructure.
Some approaches have been valuable in disease modeling and prediction. During the 2018–2020 Ebola outbreak in DRC, researchers used AI-driven models to anticipate viral spread based on mobility and contact data, allowing for better resource allocation in high-risk areas1.
Ghana’s Noguchi Memorial Institute for Medical Research piloted a model that combined rainfall, vegetation, and population density to predict malaria hotspots, which enabled more targeted interventions. These cases show AI’s ability to integrate large, complex datasets from satellites to mobile phones to forecast disease trends.
During the COVID-19 pandemic, the Partnership for Evidence-Based Response to COVID-19 (PERC) used AI metapopulation models to assess the impact of health measures across 20 African countries2. The models showed that human mobility was a more reliable predictor of new waves than raw case counts. This insight allowed governments to focus restrictions and resources on the most at-risk regions, rather than applying blanket policies. Other pilots in Senegal and Rwanda have used AI to optimize vaccine distribution and automate contact tracing, reducing delays in reaching vulnerable populations3.
Looking ahead
African health systems can improve supply chain logistics by predicting shortages of test kits, vaccines, or personal protective equipment before they occur. Behavioural modelling is another promising area, where AI analyses call centre transcripts or social media posts to understand public perceptions and misinformation during outbreaks. With this insight, communication strategies can be adjusted in real time to increase trust and compliance through workforce planning, and forecasting where and when clinical staff will be needed most based on disease trends and hospital capacity.
Many imported AI models fail because they are trained on data that doesn’t reflect African realities. As recent commentaries have highlighted, foundational models often exclude African languages and contexts, limiting their relevance1. AI tools must be developed with local ownership using African data, guided by African institutions, and deployed with local needs in mind. Public health agencies and research institutions must invest not only in the technology itself, but in building the infrastructure, talent, and policies that allow AI to work in African settings.
