Modelling seasonal patterns, climatic coherence and forecasting future of PPR in Ekiti state, Nigeria

Abstract Background/Aim: Peste des Petits Ruminants (PPR) is a highly contagious transboundary viral disease pos­ing a severe threat to small ruminant production in Nigeria. Despite its endemic status and significant economic burden in Ekiti State, no study has specifically targeted seasonal pattern identification, climatic coherence analysis, or outbreak forecasting for this region, to he best of our knowledge. This study aimed to characterise the seasonal dynamics and climatic drivers of PPR incidence in Ekiti State, Nigeria, and to generate a 20-month outbreak forecast. Methods: Monthly PPR case data (January 2015 – September 2025) obtained from the Ekiti State Ministry of Agriculture were analysed alongside NASA POWER climate data. Continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence analyses were applied to identify periodic structures and climate–disease rela­tionships. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed and validated against a 15% hold-out test set, with model selection guided by AIC and performance evaluated using RMSE, MAE, and MAPE. Results: Wavelet power spectrum analysis revealed a dominant annual cycle in PPR incidence (9–15 month band), strongest during 2015–2019, with a secondary interannual signal at 18–24 months. Cross-wavelet analysis showed significant co-movement between PPR cases and humidity (0.7–0.8 power) and temperature (0.8–0.9 power) within the 8–16 month band. Humidity was found to lead PPR cases by 3–5 months in-phase, while temperature exhibited an anti-phase relationship, with PPR cases rising during cooler periods. The SARIMA(3, 0, 3)(3, 1, 3)12 model achieved the best performance (RMSE = 0.818, MAE = 0.680, MAPE = 0.358), forecasting monthly case counts averaging 22–55 through May 2027, with seasonal troughs in January–February and peaks in Septem-ber–October. Conclusion: This study elucidates the seasonal dynamics of PPR in Ekiti State, explores its relationship with climatic variability, and forecasts future disease incidence. The find­ings provide valuable evidence for optimizing the timing of vaccination and surveillance programmes and for supporting data-driven, seasonally targeted PPR control strategies.