Persistent Disparities and Climate Vulnerabilities: Analysing Nigeria’s Malaria Burden (2010–2022) and Pathways to Climate-Resilient Control

Abstract

Background: Nigeria continues to bear the highest malaria burden globally, accounting for 27% of all cases worldwide. Despite concerted efforts, the country's progress in reducing malaria transmission and mortality rates has consistently fallen short of global targets. This persistent challenge underscores the need for advanced analytical approaches to understand and combat the disease more effectively.

Objectives: This study aims to comprehensively examine Plasmodium falciparum malaria trends across Nigeria from 2010 to 2022, with particular focus on three key indicators: mortality rates, disease incidence, and infection prevalence. By employing spatially resolved data and innovative modelling techniques, the research seeks to identify high-risk areas, evaluate temporal patterns, and develop more accurate predictive tools to support malaria control efforts.

Methods: The investigation utilized high-resolution data from the Malaria Atlas Project, incorporating spatial autocorrelation analysis through Moran's I to detect transmission hotspots. Advanced artificial intelligence techniques were implemented, including a hybrid dynamic SEI-SEIR epidemiological framework combined with Long Short-Term Memory (LSTM) neural networks. This approach addressed limitations in Nigeria's malaria data quality while improving prediction accuracy. The study also conducted scenario-based forecasting to project 2023 disease incidence under varying conditions.

Results: The findings reveal Nigeria's disproportionately high malaria burden, with 2022 mortality rates (96.35 deaths per 100,000), incidence (294.45 cases per 1,000), and childhood prevalence (26.30%) exceeding global averages by factors of 4.4, 10.5, and 8.8 respectively. Temporal analysis showed a slow annual prevalence decline of 1.1%, significantly below the global rate of 2.3%, with COVID-19 pandemic effects causing notable mortality spikes to 106.83 per 100,000 in 2020. Geospatial analysis identified persistent high-transmission zones in northern states like Sokoto and Borno, attributed to climatic factors, healthcare access limitations, and favorable mosquito breeding conditions, while urban centers such as Lagos demonstrated lower transmission rates.

Conclusion: This research demonstrates the critical need for enhanced malaria control strategies in Nigeria, emphasizing the integration of AI-powered surveillance systems with real-time climate data. Targeted interventions such as precision larviciding in flood-prone regions and mobile healthcare units for conflict-affected areas should be prioritized. The study's alignment with Nigeria's National Malaria Strategic Plan (2021-2025) provides a science-based framework for accelerating progress toward elimination targets. Addressing data quality issues, leveraging spatial-AI innovations, and tackling underlying socioeconomic determinants emerge as essential components for reducing Nigeria's disproportionate malaria burden and achieving meaningful, sustainable improvements in public health outcomes.