OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities

TL;DR

This paper presents OPTIC-ER, a reinforcement learning framework designed to improve real-time emergency response and ensure equitable resource distribution in underserved African communities, demonstrating high accuracy and practical deployment features.

Contribution

Introduces a novel RL framework with attention-guided architecture, context-rich state encoding, and a precision reward for equitable and efficient emergency response in low-resource settings.

Findings

Achieved 100% optimal action selection rate in unseen incidents.

Validated robustness and generalization in real-world data from Nigeria.

Provides infrastructure and equity monitoring tools for proactive governance.

Abstract

Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER, a reinforcement learning (RL) framework for real-time, adaptive, and equitable emergency response. OPTIC-ER uses an attention-guided actor-critic architecture to manage the complexity of dispatch environments. Its key innovations are a Context-Rich State Vector, encoding action sub-optimality, and a Precision Reward Function, which penalizes inefficiency. Training occurs in a high-fidelity simulation using real data from Rivers State, Nigeria, accelerated by a precomputed Travel Time Atlas. The system is built on the TALS framework (Thin computing, Adaptability, Low-cost, Scalability) for deployment in low-resource settings. In evaluations on 500 unseen incidents, OPTIC-ER achieved a 100.00% optimal action selection rate, confirming its robustness and generalization. Beyond dispatch, the system generates Infrastructure Deficiency Maps and Equity Monitoring Dashboards to guide proactive governance and data-informed development. This work presents a validated blueprint for AI-augmented public services, showing how context-aware RL can bridge the gap between algorithmic decision-making and measurable human impact.