Automated Haematology in Infectious Disease Diagnosis and Management: A Critical Review with Special Reference to Nigeria

Abstract

Automated haematology analysers have transformed the way clinicians detect, monitor, and manage infectious diseases, moving the complete blood count from a slow manual procedure into a rapid, multi-parameter diagnostic platform. This review examines the technological evolution of these analysers and synthesises the evidence linking specific hematological signatures to major infections of relevance to tropical and sub-Saharan settings, with particular attention to Nigeria, a country carrying one of the highest burdens of malaria, tuberculosis, human immunodeficiency virus infection, typhoid fever, and viral haemorrhagic fever in the world. The review traces the shift from impedance-based three-part differential counters to modern flow-cytometric and fluorescence-based platforms capable of generating extended research parameters, and it appraises derived indices such as the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as low-cost adjuncts for triage and prognosis. Particular emphasis is placed on the practical realities of Nigerian clinical laboratories, where workforce shortages, unstable electricity supply, and uneven access to modern analysers continue to constrain diagnostic capacity even as malaria, Lassa fever, and bloodstream infections make rapid haematological assessment clinically urgent. The review further considers artificial intelligence-enabled digital morphology systems and deep learning approaches to automated parasite detection, evaluating their potential to narrow the gap between microscopy expertise and diagnostic demand in resource-constrained settings. Persistent challenges are identified, including inconsistent reference intervals for African populations, limited external quality assurance coverage, fragile cold chains and power supplies, and the disproportionate cost of advanced platforms relative to national health budgets. The review concludes that automated haematology, when integrated thoughtfully with point-of-care innovation, digital decision support, and strengthened laboratory governance, holds substantial promise for improving infectious disease diagnosis and management in Nigeria and comparable settings, provided that technology adoption is matched by investment in infrastructure, workforce training, and standardisation.