How can artificial intelligence improve public health emergency management in resource-constrained settings?
Artificial intelligence (AI) has emerged as a promising tool for strengthening public health systems and improving Public Health Emergency Management (PHEM). Historically, digital technologies have supported disease surveillance and healthcare delivery.
More recently, AI has demonstrated its potential during public health emergencies such as the COVID-19 pandemic, where it was used to support outbreak detection, optimise vaccination site placement, improve resource allocation, and strengthen health communication. By integrating and analysing large volumes of data from multiple sources, AI can support real-time disease surveillance, predictive analytics, and evidence-based decision-making.
In South Africa, increasing pressures on the health system, recurrent infectious disease outbreaks, and inequalities in access to healthcare highlight the need for innovative and sustainable approaches to strengthening public health emergency management.
AI-enabled technologies, including predictive analytics and telemedicine, offer opportunities to improve surveillance, expand access to care, and support public health responses, particularly in underserved communities.
The following report examines the role of AI in addressing key challenges in public health emergency management in resource-constrained settings, with a focus on its applicability to strengthening health system performance in South Africa.
Approach
A narrative review of peer-reviewed and grey literature published between 2020 and 2025 was conducted using sources including PubMed, Google Scholar, WHO, and CDC reports.
Applications of AI in public health
Disease surveillance and early warning
During the COVID-19 pandemic, the United States CDC applied AI to monitor disease spread by integrating electronic health records, social media platforms, and news reports to support timely outbreak detection.
Resource allocation and response co-ordination
During COVID-19 vaccination campaigns, AI models were used to analyse demographic, health, and geographical data to identify optimal locations for vaccination sites.
Risk communication and community engagement
The WHO deployed AI-driven chatbots on platforms such as WhatsApp to provide real-time information on COVID-19 symptoms, preventive measures, and vaccination while helping to counter misinformation.
Clinical care and service delivery
AI-enabled diagnostic tools, telemedicine platforms, and drone technology have demonstrated potential to improve healthcare access and service delivery in underserved and remote communities.
Predictive analytics
AI-driven predictive analytics can support earlier identification of disease outbreaks, strengthen resource planning, and improve public health decision-making through analysis of large and complex datasets.
Taken together, the evidence suggests that AI holds meaningful potential to strengthen public health emergency management and broader health system resilience in resource-constrained settings such as South Africa. A more detailed exploration of these opportunities, alongside implementation considerations and practical recommendations, is provided in the full report.
For a more in-depth look into this thought-provoking policy brief download the full PDF below
