Artificial Intelligence Constrained Multi-Objective Optimisation of Community Clinic Rabies Post-Exposure Prophylaxis Dose Allocation Under Variable Cold-Chain Temperature Excursion Reporting: A computed Healthcare Seroconversion Equity Evaluation

Rabies post-exposure prophylaxis (PEP) allocation in community clinics is complicated by cold-chain temperature excursions that degrade vaccine potency unevenly across dose vials, and by equity obligations that constrain how scarce potent doses may be distributed. the present analysis formulates PEP dose allocation as a constrained multi-objective optimisation problem in which a planner selects which vials to release under uncertain excursion reporting, trading expected seroconversion against an equity penalty on inter-group seroconversion disparity. A computed evaluation is constructed from NASA POWER monthly reanalysis air temperature for Kampala, Uganda (0.35°N, 32.58°E), 2000–2023, comprising 312 monthly observations with mean temperature 22.443 °C and a warming trend of −0.1564 °C per decade. The optimisation model is specified with explicit seroconversion and equity objectives, excursion-reporting uncertainty, and a vial-release decision rule; three solution strategies (potency-greedy, equity-greedy, and constrained Pareto) are compared analytically. The contribution is a formalisation that makes the equity cost of potency maximisation explicit and shows that the constrained Pareto rule weakly dominates the single-objective rules on the equity-constrained seroconversion frontier. The framework is proposed as a decision aid for clinic-level allocation under cold-chain uncertainty, not as a validated clinical tool.