PREIS: A Modular, Reproducible and AI-Assisted Framework for Epidemic Intelligence Workflows in Africa

Background. Epidemic intelligence increasingly requires the integration of official surveillance information, event-based signals, open-source information, scientific evidence and operational context. Many public health teams in Africa would benefit from locally maintainable analytical layers that can transform heterogeneous inputs into reproducible, auditable and decision-oriented products while preserving expert review, institutional data governance and official communication channels.Objective. This manuscript describes PREIS (Pan-African Real-time Epidemiological Intelligence System), a modular and reproducible workflow architecture designed to support signal capture, harmonisation, risk-prioritisation, dashboard generation, situation summary preparation and conditional notification templates for epidemic intelligence use cases in Africa.Methods. This is a methodological system-design paper. PREIS is organised around five interacting layers: data-source configuration, harmonisation, intelligence processing, output generation and review-to-action feedback. The framework follows a CORI cycle - Capture, Organise, Review and Inform - and emphasises modularity, reproducibility, provenance tracking, human-in-the-loop review, cautious risk communication and data protection. It is intended to be implemented using open analytical environments such as R, Python and Shiny, with configuration files defining disease themes, geographic scope, source permissions, classification rules, quality checks and notification thresholds.Results. The proposed framework defines a minimum intelligence data model, quality gates, configurable scoring dimensions, dashboard-ready outputs and governance safeguards. Proof-of-concept development has shown the feasibility of standardised data append workflows, country and regional classification, quality-control logging, prototype dashboards, illustrative risk maps and notification templates. These outputs are methodological demonstrations; they are not official outbreak alerts and have not yet undergone prospective external validation against verified epidemiological ground truth.Conclusions. PREIS is best understood as an adaptable analytical layer that may complement, rather than replace, established surveillance frameworks and platforms. Its contribution is to document a reproducible workflow model that can help public health practitioners organise heterogeneous signals into transparent and reviewable intelligence products. Future work should focus on co-design with authorised institutions, legal data-sharing arrangements, prospective validation, performance evaluation and disease-specific peer-reviewed studies.Keywords: epidemic intelligence; event-based surveillance; public health surveillance; Africa; reproducible analytics; dashboard; risk prioritisation; open-source intelligence; data governance; human-in-the-loop; PREIS