AI-DRIVEN COLD CHAIN MONITORING SYSTEMS AND THE PRESERVATION AND DISTRIBUTION OF VACCINES

Abstract

The preservation and distribution of vaccines remain a critical challenge in Nigeria’s Primary Health Care (PHC) system, where infrastructural limitations such as unreliable electricity, weak monitoring systems, and poor logistics undermine immunization programs. This study examines Artificial Intelligence (AI)-driven cold chain monitoring systems and preservation and distribution of vaccines in PHCs within the Federal Capital Territory (FCT), Abuja. Anchored on the Resource-Based View (RBV) theory, the study operationalizes AI through three proxies: machine learning, real time data analysis, and IOT sensor and evaluates their effects on vaccine spoilage, distribution efficiency, and availability. A case study research design was adopted with a sample of 300 immunization staff selected through stratified random sampling. Data were collected using structured questionnaires, semi-structured interviews, and observation checklists, and analyzed using descriptive and inferential statistics. Findings revealed that vaccine spoilage declined by 69% following AI adoption, while delivery lead time improved from 3.9 to 2.5 days and stock-outs dropped from 14% to 5%. Multiple regression analysis confirmed that AI proxies explained 68% of the variation in cold chain performance, with real time data analysis having the strongest effect (β = 0.452). The study concludes that AI significantly strengthens vaccine cold chain systems by reducing wastage, improving efficiency, and enhancing availability in PHCs. However, challenges such as unstable electricity supply, limited digital literacy among PHC staff, and inadequate funding constrain sustainability. It recommends scaling up AI deployment nationwide, investing in capacity building, and fostering collaborations between government and technology partners to achieve equitable and resilient vaccine distribution across Nigeria.