Use of Geospatial Big Data Intelligence Software for Epidemic Forecasting and Public Health Decision-Making:

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