Emmanuel Afolabi Bakare

Background: Malaria is a critical public health concern in Nigeria with the country bearing an uneven burden of the disease. In Nigeria, malaria is one of the main causes of child mortality and despite all efforts to reduce malaria mortality rates, the disease remains a major concern, notably among children under‐fives. There is a paucity of data on more localized predictive malaria risk geospatial maps to inform control and elimination strategies amidst limited public health resources in this setting. This modelling study therefore sought to understand, predict and map malaria risk in the presence of environmental factors in Nigeria. Methods: This study utilized data from the 2021 Nigeria Malaria Indicator Survey (NMIS), conducted under the Demographic and Health Surveys (DHS) Program. The 2021 NMIS marks the third malaria indicator survey carried out in Nigeria, following previous surveys in 2010 and 2015. The outcome variable of interest is the number of individuals in each sampled cluster who tested positive on the rapid diagnostic test (RDT). This study investigated spatial risk factors for malaria prevalence in Nigeria through geostatistical modelling approaches. The implementation of the models was carried out with the integrated nested Laplace approximation (INLA) method via the stochastic partial differential equation (SPDE) approach in R‐INLA. Results: The study identified aridity (log‐odds = −0.0400, 95% CrI = −0.0610, −0.0190) and enhanced vegetation index (log‐odds = 9.3930, 95% CrI = 7.4220, 11.3760) as significant predictors of under‐five malaria risk. The fitted Bayesian geospatial spatial model with covariates predicted malaria prevalence of 24.9% with a range of 0.5% to 74.2%. The predicted malaria prevalence was highest in parts of Zamfara (prevalence > 70%) and Kebbi (prevalence > 60%) States. Conclusion: These findings are of the utmost significance for policymakers involved in malaria control and elimination efforts. They provide evidence‐based information that can guide resource mobilization and targeting, intervention design, and monitoring strategies. The identification of environmental predictors like aridity and enhanced vegetation index suggests the need for location‐specific interventions according to environmental determinants of malaria transmission.

Malaria remains a life-threatening disease and poses a significant economic burden in sub-Saharan Africa, with Nigeria accounting for the highest global morbidity and mortality. Despite increased preventive and control interventions implemented by the National Malaria Elimination Programme over the years, malaria transmission in Nigeria continues to exhibit spatial and temporal dynamics making elimination difficult to achieve. Understanding malaria seasonal patterns and synchrony between cases in different transmission settings in Nigeria, and forecasting future outbreaks is therefore critical for guiding public health policies. Time series modelling plays a crucial role in extracting meaningful insights, revealing patterns and forecasting infectious disease outbreaks. In this study, we employed two time series approaches-wavelet analysis and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model-to analyse confirmed uncomplicated malaria case data of Nasarawa, Kwara, and Zamfara States, Nigeria, over a 10-year period (January 2015 to December 2024). Wavelet analysis decomposed the time series data into their constituent time-frequency components, enabling multi-resolution analysis of the data and revealing hidden patterns. The SARIMA model, on the other hand, was used to forecast malaria cases in each of the three states for the next two years (January 2025 to December 2026). Univariate wavelet analysis showed that malaria incidence in Nasarawa followed a very strong annual cycle throughout almost the entire study period, with seasonal peaks occurring mostly in September 70% of the time. In Kwara State, short periods of semi-annual, annual, and multi-annual cycles were detected, with annual peaks mostly appearing in August for about half of the study period. In Zamfara, a moderate yearly cycle was observed between 2015 and June 2020 and again from 2022 to 2023, with seasonal peaks occurring consistently in September, except in 2024, when the peak shifted to August. Bivariate wavelet analysis showed that Nasarawa and Zamfara had an almost perfectly synchronized seasonal malaria pattern for most of the study period. For both the Nasarawa-Kwara and Zamfara-Kwara pairs, synchrony was present only between mid-2018 and early-2020; outside this period, Kwara’s malaria peaks occurred earlier in the season in both cases. For the period 2025–2026, the SARIMA model forecasted average monthly malaria cases of 51,482 (95% CI: 23,899-116,073) in Nasarawa; 20,850 (95% CI: 3,381-202,511) in Kwara; and 67,463 (95% CI: 27,317-171,418) in Zamfara. These findings emphasize the need for state-specific malaria interventions to capture the variability observed in Kwara, and regionally coordinated control measures for Nasarawa and Zamfara where very strong synchrony exists. They also provide useful guide for policymakers on optimally timing interventions in line with observed seasonal patterns.

Malaria remains a major public health challenge in Nigeria, and increasing climate variability poses substantial threats to recent gains in control. However, malaria transmission does not respond uniformly to climate drivers across epidemiological settings, highlighting the need to explore climate-malaria dynamics within heterogeneous contexts. This study examined the non-stationary temporal dynamics of malaria incidence and two key climatic drivers—rainfall and temperature—in Lagos and Zamfara states. These states were selected to represent heterogeneous transmission intensities, urbanisation and climatic regimes. Monthly malaria incidence and corresponding climate data (2015–2024) were analysed using wavelet-based model to characterise the non-stationary periodicities, quantify time-varying climate–malaria associations and identify time-dependent lead–lag relationships. Malaria incidence exhibited transient semi-annual, annual, and multi-annual cycles that were weak and temporally localized, despite persistent annual cycles in rainfall and temperature in Lagos. Cross-wavelet spectra revealed intermittent associations within the 8–16-month band, while phase analysis indicated short-lived alignment in which malaria incidence lagged rainfall by approximately one month, particularly between 2019 and 2022. The relationship with temperature was unstable, suggesting rainfall exerted more consistent influence on malaria incidence. In contrast, Zamfara displayed strong and dominant annual cycles of malaria incidence throughout the study period, with rainfall and temperature showing stable, statistically significant annual co-variability. Phase analysis revealed malaria incidence lagged rainfall by approximately one month and temperature by approximately three to four months, consistent with climate-modulated transmission processes. These findings highlight the heterogeneity of climate-malaria dynamics across transmission settings with contrasting epidemiological implications within Nigeria. The observed lag structures provide a basis for climate-informed early warning systems and intervention timing. While non-climatic drivers were not explicitly modelled, the analysis focuses on isolating climate-driven temporal signals. Consequently, to sustain control and elimination progress, climate-adaptive surveillance and region-specific interventions that anticipate rainfall- and temperature-driven transmission cycles must be integrated into Nigeria’s malaria control framework to ensure timely, targeted, and climate-resilient public health responses. Author summary: Malaria transmission does not respond uniformly to climate drivers across epidemiological settings, highlighting the need to explore climate-malaria dynamics within heterogeneous contexts. Identical climatic forcing can produce qualitatively different outcomes depending on the underlying epidemiological setting, indicating the limitations of generalising control efforts from a single context. Motivated by the need to understand these differences, in this study, we examined the cross-epidemiological scale-dependent and lag-specific climatic forcing of malaria transmission at the sub-national context, providing support for malaria control and elimination strategies. We addressed the following questions to understand the hidden patterns of the temporal cycles and the corresponding associations between the climate variables and malaria incidence in the two states: What are the dominant temporal cycles in malaria incidence in the study region? How do the periodicities of climate variables compare with those of malaria incidence? Are there significant time-dependent associations between climate variability and malaria incidence? How do these association vary across different time scales (intra-annual vs interannual) and periods? What is the average lag between changes in key climate variables and malaria incidence? Monthly malaria incidence data and corresponding rainfall and temperature records spanning 2015–2024 were analysed using a continuous wavelet transform (CWT) framework. Scale-specific periodicities were identified using wavelet power spectra, while climate–malaria associations were quantified using cross-wavelet power and wavelet transform coherence (WTC). Phase difference analysis was employed to characterise time-varying lead–lag relationships between malaria incidence and climatic drivers at the annual timescale. Results show that in Lagos, malaria incidence is irregular and weakly linked to climate, reflecting the impact of interventions and socio-environmental factors that disrupt transmission. In contrast, Zamfara exhibits strong, regular annual cycles tightly coupled to rainfall and temperature, with malaria incidence lagging rainfall by about one month and temperature by three to four months. These findings highlight the need for region-specific strategies: sustaining intervention-driven disruption in low-burden urban areas, and intensifying climate-adaptive measures in high-burden rural settings. Integrating climate-sensitive surveillance and tailored intervention timing into Nigeria’s malaria control framework will strengthen resilience and accelerate progress toward elimination. Specifically, our findings demonstrate evidence-based framework to guide climate-adaptive intervention timing. In Zamfara state, extreme heat between March and May as shown in the temperature profile, may reduce use of LLINs, indicating that mass distribution before and during these periods, within same year, may be less effective. The start of rain comes with a cooling effect which may facilitates good weather condition that encourages LLIN utilization. Correspondingly, LLIN distribution campaigns conducted in June or July, prior to peak rainfall and peak malaria incidence typically observed between August and October, may enhance intervention effectiveness. Coupled with other...

Background: Malaria, transmitted by female Anopheles mosquitoes, remains a major public health challenge in Nigeria, where approximately 97% of the population is at risk. Despite large-scale investments, Nigeria continues to bear the world's highest malaria burden. Long-lasting insecticidal nets (LLINs) are central to prevention, yet their effectiveness is increasingly undermined by non-usage, delayed replacement, and growing outdoor biting activity. National surveys (MIS, PMI) consistently report gaps in LLIN use, irregular implementation of the three-year replacement strategy, and persistent outdoor biting. This study quantifies the relative contributions of these behavioural and entomological factors to sustained malaria transmission across five Nigerian states. Methods: A deterministic compartmental model of malaria transmission was developed and calibrated using Bayesian inference with MCMC in CmdStanR. The model incorporated heterogeneous mosquito biting behaviour, LLIN effectiveness decay, and distribution cycles. Calibration used monthly malaria case data (2015-2024), demographic and entomological data (2015-2022), and DHS/MIS prevalence surveys (2015, 2018, 2021) for Akwa Ibom, Ebonyi, Kebbi, Oyo, and Plateau states. Counterfactual scenarios quantified malaria cases attributable to (i) outdoor mosquito biting, (ii) LLIN usage gaps, and (iii) delayed replacement. Parameter sweeps were used to assess how LLIN effectiveness changes with varying outdoor biting intensities. Results: Eliminating outdoor biting yielded the largest reductions in malaria incidence-Akwa Ibom (82.4%, 95% CrI: 74.0-89.6), Ebonyi (92.0%, 95% CrI: 87.5-95.5),Kebbi (76.4%, 95% CrI: 51.0-92.6), and Oyo (83.0%, 95% CrI: 74.9-89.6). LLINs substantially reduced malaria transmission only under low outdoor biting intensities-below 1 bite per mosquito per month in Akwa Ibom and Ebonyi, below 0.2 in Kebbi and up to 0.8 in Oyo. In Plateau, outdoor biting contributed minimally (5.8%, 95% CrI: 4.8-6.3), while the gap between LLIN ownership and use was the dominant factor (23.1%, 95% CrI:22.5-23.8), rising to 36.5% (95% CrI: 35.5-37.6) when combined with delayed replacement. Conclusion: Outdoor mosquito biting is a dominant driver of persistent malaria transmission in Akwa Ibom, Ebonyi, Kebbi, and Oyo states, whereas low LLIN usage is the leading factor in Plateau. Although maintaining high LLIN coverage, adherence, and timely replacement remains critical, these efforts alone are insufficient where outdoor biting is widespread. Strengthening Nigeria's malaria control strategy will require integrating LLIN deployment with targeted outdoor vector control and state-specific behavioural interventions to achieve sustained reductions in malaria burden. Keywords: Plasmodium falciparum, malaria transmission, outdoor mosquito biting, long-lasting insecticidal nets (LLINs), behavioural factors, Nigeria.

\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).