The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area outperform conventional surveillance systems in developing countries.
Abstract
Background
Surveillance of infectious diseases remains a persistent challenge in many countries across the world. Most of these countries have slow reporting systems, poor infrastructure of data, and a limited capability of predictive analytics. Conventional surveillance methods are reactive, which leads to delayed outbreak containment. The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area.
Methods
A longitudinal dataset comprising 152,438 observations (2020-2024) across 30 LGAs in six states was analyzed. The confirmed figures of COVID-19, Cholera, Lassa Fever, Malaria and Ebola, mobility index, temperature, rainfall, population density and location data. The study utilized Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), and ARIMA. We evaluated the performance of our model based on Accuracy, Precision and Recall. A module for the detection of anomalies.
Results
According to the results, Random Forest had the highest predictive performance (AUC = 0.94; F1-score = 0.91) followed by Gradient Boosting (AUC = 0.92; F1-score = 0.89). Moderate performance (AUC = 0.84) by Logistic Regression. ARIMA was able to capture trends over time but considerably underperformed at outbreak spikes (RMSE = 18.7). Mobility index and rainfall were necessary predictors (p < 0.05). Detection framework identified outbreak spikes with 93% sensitivity.
Conclusion
Machine learning early warning systems outperform conventional surveillance systems in developing countries. The fusion of environmental and mobility variables helps in prediction and containment at source. The framework provides a huge scalable and efficient resource model for digital disease surveillance in countries
A comprehensive review of CI models for outbreak prediction, comparing supervised and unsupervised methods such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and hybrid models.
Z. Abdullahi· International Journal of App...· 0 citations
Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.
A machine learning-based model for predicting outbreaks, explainability, and spatial risk propagation, validated through a multiyear data set of an epidemiological nature from 12 cities in the Eastern Province of Saudi Arabia (2021–2025).
N. F. Saleem ALAnsary, Mahmood Berekaa, Raghad Alhotheyfa et al.· Frontiers in Public Health· 0 citations
Lassa fever remains a critical and highly perilous public health threat across West Africa, with Ondo State, Nigeria, consistently experiencing severe annual outbreaks. While accurate forecasting is essential for timely medical intervention, conventional predictive models often fail to capture the complex spatiotemporal transmission dynamics and the delayed ecological triggers of the Lassa virus. To address this, this study introduces a comprehensive Health Information Pattern Discovery framework to forecast outbreaks across four high-risk localized hotspots: Akure South, Akure North, Akoko Southwest, and Owo. Advancing beyond standard machine learning benchmarking, this research integrates spatial-temporal graph theoretic modelling to map the relational transmission velocity between regions , coupled with a Genetic Algorithm (GA) to optimize complex lagged meteorological variables, including temperature, relative humidity, and precipitation. The optimized spatiotemporal feature space was utilized to train and evaluate four distinct architectures: Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM) networks. Empirical evaluation demonstrated that the GA-optimized Random Forest ensemble outperformed the other models, achieving a Root Mean Squared Error (RMSE) of 5.627, a Mean Absolute Error (MAE) of 3.747, and an of 0.87. Beyond baseline predictive accuracy, this hybrid graph-theoretic and evolutionary approach successfully isolates the exact environmental triggers preceding an outbreak, providing interpretable, actionable intelligence to strengthen proactive public health planning and targeted interventions in endemic regions.
Unknown authors· Journal of Science, Technolo...· 0 citations
Background: Evaluating outbreak detection models is a key component of syndromic surveillance. However, balancing timeliness, predictive performance, and local surveillance constraints remains a major challenge. We developed and assessed whether stacking ensemble approaches, which integrate multiple outbreak detection methods, can improve the timeliness and predictive performance of influenza-like illness (ILI) surge detection. Methods: We developed a two-stage stacking ensemble framework to detect early warning of ILI surges in city-level Primary Health Care encounter time series from Brazil (2022 to 2025). Epidemic thresholds were defined using the Moving Epidemic Method (MEM). In the first stage, multiple outbreak detection models (ODMs) generated warnings of unusual ILI activity. In the second, these warnings were then used as inputs to three supervised meta-classifiers: Logistic Regression, Extreme Gradient Boosting (XGB), and a Multi-layer Perceptron (MLP). For comparison, a Majority Voting (MV) aggregation is also examined. Timeliness, sensitivity, specificity, positive and negative predictive values are evaluated to measure each model's ability to anticipate epidemic periods of varying intensity in 2025. Robustness was further assessed using simulated outbreak scenarios with varying magnitudes and durations. Findings: We identified 5,765 ILI surge onsets across 5,365 Brazilian municipalities in 2025. Compared with individual ODMs and MV, stacking ensemble meta-classifiers anticipated up to 33% of surge onsets three weeks in advance (an average improvement of 15 percentage points) while reducing missed detections to <10%. They achieved sensitivity >90%, while maintaining balanced specificity >80%, PPV >65%, and NPV >99%. Improvements were greatest for very high-intensity surges, with missed detections reduced by more than half compared with individual ODMs. In simulated outbreak scenarios, the MLP and XGB classifiers remained robust despite being trained on fewer than half of all simulated surge events, consistently outperforming individual detection methods and simpler integration approaches. Interpretation: We provide a practical framework for integrating complementary ODMs into a single, robust early warning decision. By improving both timeliness and predictive performance without requiring additional surveillance data or resources, this approach offers a scalable methodological upgrade for syndromic surveillance systems and supports more reliable public health decision-making. Funding: The Rockefeller Foundation (award 2023 PPI 007 to MB-N); Brazilian National Research Council - CNPq (408775/2024-6); MB-N, PIPR, RFSA are CNPq fellows.
J. F. Oliveira, A. L. Alencar, E. R. Coutinho et al.· medRxiv· 0 citations
Vector-borne diseases like dengue, chikungunya, and malaria continue to be a significant public health concern in India, especially in climatically sensitive areas where seasonal effects have a strong impact on disease transmission. Existing national surveillance systems are largely reactive, detecting outbreaks only after case counts rise substantially. This work proposes the CLARION-RF model, a probabilistic framework that is climate-lag aware and capable of early outbreak-risk prediction at the district level. The model combines time-series data from the weekly disease surveillance system with corresponding meteorological variables and climate lag features. The Random Forest-based probabilistic model is trained by balancing the samples to address class imbalance. Instead of deterministic predictions, the model estimates outbreak probabilities and stratifies them into three risk levels: low, medium, and high. Time-aware training (2011–2023) and testing in 2024 demonstrate stable predictive performance, with ROC-AUC and PR-AUC of 0.667 and 0.579, respectively, for the best model.
Manikandaprabhu Perumalsamy, Gururaghavendra, Yashwanth Kumar M et al.· International Conference Com...· 0 citations
