Background: Childhood anaemia remains a significant public health challenge, particularly in low- and middle-income countries like Nigeria, where the prevalence among children under five is alarmingly high. This study aims to identify the key determinants of childhood anaemia, develop an accurate predictive model using advanced machine learning techniques, and assess the model’s performance across different demographic groups to ensure equitable risk prediction. Methods: Data from 13,136 children aged 6-59 months from the 2018 National Demographic Health Survey (NDHS) were analysed. Sixteen machine learning algorithms were evaluated based on their ability to predict childhood anaemia using a wide range of individual, community, and environmental factors. The Extra Trees (ET) classifier, demonstrating the highest predictive performance, was used to identify the top ten predictors of childhood anaemia. A fairness and demographic bias assessment framework was incorporated to evaluate the model’s performance across different regions, wealth index categories, ethnic groups, and gender. Results: The ET classifier outperformed all other algorithms, achieving an area under the curve (AUC) of 0.8319, accuracy of 0.7565, and a recall of 0.7565. The top ten predictors identified by the ET model included the number of under-five children in the household, birth order, child age, media access, maternal health-seeking behaviour, child gender, proximity to water, money problems, day land surface temperature, and all population count. The demographic bias assessment revealed variations in model performance across different subgroups, with the lowest AUCs observed in the North-East region (0.79), the poorest wealth index category (0.80), and the Hausa/Fulani ethnic group (0.81). Conclusion: This study demonstrates the potential of machine learning techniques to accurately predict childhood anaemia in Nigeria and identify key risk factors that inform targeted interventions. Future research should focus on refining the predictive model, exploring integrated interventions, and deploying AI-based tools to combat childhood anaemia in Nigeria and beyond.

The COVID-19 pandemic, originating in Wuhan, China, in late 2019, swiftly escalated into a global health crisis by March 2020, severely impacting nations worldwide. The World Health Organization (WHO) faced criticism for delayed responses and underreporting, particularly from China, compounded by geopolitical tensions and funding shortages. This constrained the WHO’s ability to effectively manage the pandemic. Additionally, national responses varied significantly, influencing outcomes. Key factors contributing to the state’s failure to address COVID-19 effectively include delayed government actions, governance and leadership failures, poor communication, and inadequate economic support. Early and stringent measures, as demonstrated by New Zealand, resulted in better outcomes compared to countries like Italy and the United States, which experienced severe outbreaks due to delayed responses and inconsistent communication. Governance issues, such as the lack of cohesive strategies and leadership coordination, were evident in countries like Indonesia and Japan, leading to inconsistent policy implementation and poor outcomes. Economic support measures played a critical role in public compliance and economic stability. Countries like the UK provided substantial financial aid, yet disparities in support led to ongoing struggles for low-income families. In contrast, countries in the Global South faced significant challenges in providing adequate economic support, exacerbating existing inequalities and complicating pandemic management. Recommendations for future responses include improving targeted health interventions, enhancing community engagement in policy-making, increasing international financial and technical support, and revising global health treaties to ensure equitable resource access. These steps are essential to build more resilient health systems capable of effectively managing future global health crises.