“Assessing the Feasibility and Impact of AI-Driven Disease Surveillance Systems in Lusaka Province, Zambia”
by Zanga Musakuzi (Dip Pharm,Bsc Pharm, Cert Pro Mangmt, MPH-C)
Effective disease surveillance is essential for identifying, tracking, and controlling infectious disease outbreaks, especially in low-resource settings where healthcare infrastructure may be limited (Heymann et al., 2019). In many African countries, including Zambia, disease surveillance systems face significant challenges that hinder their ability to detect outbreaks promptly and accurately.
Traditional disease surveillance systems in Zambia rely heavily on manual data collection, reporting, and analysis, which often leads to slow response times and incomplete data (World Health Organization, 2020; Kariuki et al., 2017). These limitations, in turn, result in delayed interventions, exacerbating disease spread and increasing mortality rates, particularly for diseases with high transmission potential, such as cholera and malaria (Mwangi et al., 2021). The problem is particularly critical in densely populated and resource-constrained areas like Lusaka Province, where rapid response to infectious diseases is essential to mitigate widespread outbreaks.
Recent advances in Artificial Intelligence (AI) have shown promising potential to revolutionize disease surveillance and epidemiology (Choi et al., 2021).
AI-driven disease surveillance systems can enable real-time data collection, predictive modeling, and rapid decision-making—factors that are crucial for effective disease prevention and control (Yu et al., 2018). AI algorithms, particularly those based on machine learning, are capable of processing vast amounts of health data more quickly and accurately than traditional methods. This enables timely identification of disease patterns and provides early warning signals for potential outbreaks (Iacobucci et al., 2022). Furthermore, AI systems can analyze data from multiple sources, including electronic health records, laboratory reports, and community health reports, to create a comprehensive view of disease spread and predict outbreaks before they escalate (Bresnick, 2020).
The objective of this research is to assess the feasibility and impact of implementing an AI-driven disease surveillance system in Lusaka Province, Zambia.
The proposed AI-based system aims to enhance disease outbreak detection, improve response times, and contribute to public health outcomes. Lusaka Province has been selected as the focus area for this study due to its high population density and significant public health challenges, making it an ideal region for piloting an advanced surveillance solution (Chanda et al., 2020). By evaluating the feasibility and potential impact of AI in disease surveillance, this research seeks to provide a scalable model that could be adopted across Zambia and in other African countries facing similar public health challenges.
This study aims to contribute to the growing body of research on AI in public health by exploring its application in resource-constrained environments and assessing its practical implications for health surveillance