DETECTION AND PREDICTION OF COLD CHAIN FAILURE IN VACCINE DISTRIBUTION USING EMBEDDED SENSOR MATERIALS AND WIRELESS SIGNAL VARIABILITY

We conducted a simulation-based package-level technical study that coupled thermo-responsive sensor materials, route-resolved ambient exposure, package-core thermal lag, humidity-sensitive degradation kinetics, and wireless feature extraction into a single framework for vaccine cold-chain integrity assessment. The dataset comprised 1,200 twenty-four-hour last-mile routes with five-minute resolution and three environmental classes. The embedded material layer was modeled as a switching polymer-composite laminate whose impedance changed sharply near the upper cold-chain boundary. We translated the material state into a passive wireless signature and used the resulting signal, together with route-derived features, to classify failure states defined by potency loss, excursion duration, and severe peak temperature. The full predictor achieved area under the ROC curve 0.935, average precision 0.914, sensitivity 0.850, specificity 0.855, and Brier score 0.102. In the representative hot-humid route, ambient temperature reached 28.3°C, the package core peaked at 22.1°C, the wireless signature swung by 5.69 units, and the final relative potency fell to 0.9873. Beyond headline accuracy, the study showed that signal variability contributed most when routes hovered near actionable thresholds rather than during catastrophic failure alone. The paper presents a package-centric cold-chain surveillance architecture that integrates route exposure, material switching behavior, potency loss, passive wireless readout, and receiving-point triage into a single technical workflow.