Enhancing disease surveillance in Nigeria through machine learning: opportunities, challenges and strategic recommendations
TL;DR
This paper presents a methodological illustration of malaria surveillance in Northern Nigeria using synthetic data, benchmarking five ML models under temporal validation, and employing SHapley Additive exPlanations for robust model interpretability, and proposes a tiered strategic framework encompassing policy, infrastructure, and capacity-building recommendations.
Abstract
Disease surveillance is fundamental to public health, enabling timely outbreak detection, efficient resource allocation, and evidence-based policymaking. In Nigeria, the Integrated Disease Surveillance and Response (IDSR) framework, while structured, is hampered by inconsistent data quality, limited private sector participation, and infrastructural constraints. Machine Learning (ML), a powerful subset of Artificial Intelligence (AI), offers transformative potential through its capacity for predictive analytics, real-time data processing, and automated pattern recognition to enhance surveillance capabilities. Despite global advancements in ML for disease forecasting and syndromic surveillance, its adoption within Nigeria’s IDSR system lags considerably. This paper addresses this critical gap by investigating how ML can overcome Nigeria-specific barriers, such as fragmented data systems and rural connectivity deficits. We provide enhanced technical depth on ML methodologies, comparing supervised and unsupervised learning, and detailing relevant architectures, including Decision Trees, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), suited for time-series epidemiological forecasting. We present a methodological illustration of malaria surveillance in Northern Nigeria using synthetic data, benchmarking five ML models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine) under temporal validation, and employing SHapley Additive exPlanations (SHAP) for robust model interpretability. A sensitivity analysis further examines the stability of model performance under coefficient perturbations. A benchmarking analysis compares Nigeria’s ML adoption against Rwanda and Kenya. Finally, we propose a tiered strategic framework encompassing policy, infrastructure, and capacity-building recommendations, complemented by a cost-benefit perspective emphasizing potential Disability-Adjusted Life Years (DALYs) averted and significant economic returns, aiming to foster a more resilient and equitable public health system in Nigeria. Not applicable.