Climate-based predictive modeling of malaria incidence using statistical and machine learning approaches

Abstract

Malaria remains a major public health burden in Nigeria, where climatic variability plays a critical role in shaping transmission dynamics. This study develops and evaluates climate-based predictive models for malaria incidence by integrating historical malaria surveillance data (2018–2023) with key meteorological variables, temperature, precipitation, humidity, and wind speed, across diverse ecological zones. Both traditional statistical and advanced machine learning (ML) approaches were employed to capture linear and nonlinear relationships between climate factors and malaria occurrence. Multiple Linear Regression (MLR) served as the baseline model, while Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Regression (GBR), XGBoost, and Long Short-Term Memory (LSTM) networks represented ML alternatives. Model performance was assessed using RMSE, MAE, R², and MAPE. Results revealed that ensemble-based ML models significantly outperformed MLR, with XGBoost emerging as the best performer (R² = 0.89; RMSE = 27.9; MAPE = 9.8%), followed closely by GBR and RF. The LSTM model effectively captured temporal dependencies (R² = 0.83), while MLR exhibited limited predictive ability (R² = 0.61). Regional analyses indicated that prediction accuracy was higher in areas with stable climatic conditions and reliable data reporting, whereas variability and data gaps in conflict-affected zones reduced performance. The findings highlight the superior predictive power and adaptability of ensemble ML methods for climate-driven malaria forecasting. The study offers an evidence-based framework for integrating these models into Nigeria’s early warning systems, supporting timely and geographically targeted malaria control interventions.