Fig 1.
Map of Nigeria showing Nasarawa, Kwara and Zamfara States.
This figure was created by the authors using R programming software (R version 4.4.3). Available at (https://www.R-project.org/). The shapefile was obtained from openly available country boundary data (Administrative boundary data) from Global Administrative Areas (GADM) (https://gadm.org/license.html), which permits academic use.
Fig 2.
Morlet wavelets at different: (a) scales and (b) translations (shifts) (Source: [33]).
Fig 3.
Wavelet analysis of the transient relationship between malaria incidence in Nasarawa, Kwara and Zamfara from January 2015 to December 2024.
Left panel: Local wavelet power spectrum of malaria incidence in a. Nasarawa c. Kwara and e. Zamfara States. Right panel: Global wavelet power spectrum of malaria incidence in b. Nasarawa d. Kwara and f. Zamfara States. The black dashed contours 5% significance levels against red noise and the white line indicates the cone of influence that delimits the region not influenced by edge effects.
Table 1.
Annual peak malaria incidence months (2015–2024) in Nasarawa, Kwara, and Zamfara.
Fig 4.
Left panel (a, c, e) show the reconstruction of malaria incidence time series based on the annual cycle (9–15 periodic band) for Nasarawa, Kwara, and Zamfara, respectively.
Right panel (b, d, f) display the corresponding phase angles of the annual cycle for each state.
Fig 5.
Left panel: Cross-wavelet power spectrum of malaria incidence between a. Nasarawa and Kwara c. Nasarawa and Zamfara, and e. Zamfara and Kwara.
Right panel: Phase difference between b. Nasarawa and Kwara d. Nasarawa and Zamfara, and f. Zamfara and Kwara. The black thick contours indicate 5% significance levels against red noise and the white line indicates the cone of influence that delimits the region not influenced by edge effects.
Table 2.
Pearson correlation coefficients quantifying synchrony between state-level malaria incidence, based on both the normalised time series and the annual cycles.
Fig 6.
Left panel: Time series plots of normalised malaria cases in a. Nasarawa, c. Kwara and e. Zamfara States. Right panel: Corresponding decomposition plots for b. Nasarawa d. Kwara and f. Zamfara States.
Fig 7.
Evaluation of SARIMA model performance on the 2023–2024 test set.
The left panel (a, c, e) presents a comparison of the forecast error metrics (MAE, MAPE, and RMSE) across candidate models for Nasarawa, Kwara, and Zamfara States, respectively, where the model with the lowest error metrics was selected. The right panel (b, d, f) displays the corresponding time-series forecasts compared against the actual test data.
Fig 8.
Left panel: Residuals of Nasarawa, Kwara and Zamfara States.
Right panel: Forecast plots of Nasarawa, Kwara and Zamfara States.
Table 3.
24-month forecast of confirmed uncomplicated malaria cases in Nasarawa State.
Table 4.
24-month forecast of confirmed uncomplicated malaria cases in Kwara State.
Table 5.
24-month forecast of confirmed uncomplicated malaria cases in Zamfara state.