Wavelet analysis and time series forecasting of malaria incidence across different transmission settings in Nigeria

Abstract

Abstract \noindent Malaria remains a public health challenge in Nigeria, characterised by high endemicity with seasonal and perennial transmission, varying between regions. Existing control programmes often face challenges related to the allocation of resources and the timing of interventions, which may be due to an inadequate understanding of local seasonal transmission patterns. Hence, this study analysed the temporal patterns of malaria incidence in Bauchi, Yobe, and Ogun states (representing high, low, and moderate transmission settings) to identify hidden patterns in non-stationary time series and forecast trends for targeted interventions. Using time series, Wavelet, and SARIMA methods, strong annual peaks in Bauchi and Yobe during September/October and stable transmission in Ogun with peaks in July were identified. Wavelet analysis identified a dominant 12-month periodicity in all selected states, strong synchrony between Bauchi and Yobe, with Bauchi leading approximately a week. On the other hand, Ogun is observed to lead the northern states (Bauchi and Yobe) by approximately 2 months. The SARIMA models predicted persistent cyclical patterns into 2026, with monthly cases of 52,000-125,000 in Bauchi, 54,000-185,000 in Yobe, and 20,000-32,000 in Ogun, peaking in October for Bauchi/Yobe and July for Ogun. This study recommends harmonised Seasonal Malaria Chemoprevention (SMC) campaigns in Bauchi and Yobe beginning before September to maximise protection during peak transmission, mass distribution of long-lasting insecticide net (LLIN) in Ogun by May / June to precede peak transmission in July, and the use of predictions as an early warning system for health resource planning, improving data-driven control strategies for the National Malaria Elimination Programme (NMEP).