Predicting Global Disease Outbreaks: A Multidisciplinary Framework

Somkenechi Chinwe Okpala¹, Cynthia Onah Ogoma², Charles Chikwendu Okpala³

¹ Consultant, Paediatrics Department, University of Nigeria Teaching Hospital, Ituku/Ozalla, Enugu, Nigeria.

² Consultant, Paediatrics Department, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria.

³ Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.  

Abstract

The increasing frequency and complexity of infectious disease outbreaks demand surveillance systems that are predictive, scalable, and aligned with sustainability objectives. This study presents a multidisciplinary, data-driven framework for the prediction of global disease outbreaks through the integration of multi-modal Artificial Intelligence (AI) with real-time big data streams that span epidemiological, environmental, climate, human mobility, genomic, and digital epidemiology domains. The framework employs a hybrid AI architecture that combines graph neural networks, temporal transformers, convolutional neural networks, and Bayesian probabilistic models to capture nonlinear spatiotemporal transmission dynamics and quantify predictive uncertainty. Using multi-region case studies on seasonal influenza, dengue fever, and COVID-19–like respiratory outbreaks, the proposed framework achieved up to 32% higher predictive accuracy when compared with traditional compartmental models and 13–23% improvements over single-modal machine learning baselines. Early-warning lead times improved by 7–14 days, thus enabling more timely and targeted interventions. Importantly, these predictive gains translated into measurable sustainability benefits, including 18–25% reductions in emergency healthcare utilization, 25–35% reductions in carbon emissions that are associated with reactive response logistics, and approximately 30% lower computational energy consumption per forecast cycle. Through explicit embedding of sustainability metrics into outbreak prediction and decision support, the research demonstrated that early, AI-driven surveillance can simultaneously strengthen global health preparedness and reduce environmental and resource burdens. The proposed framework provides a scalable and adaptable blueprint for next-generation, sustainability-aware disease surveillance systems that are capable of supporting resilient and equitable global health governance.    

Keywords: Disease outbreak prediction, Multi-modal artificial intelligence, Real-time big data analytics, Sustainable public health surveillance, Digital epidemiology, Climate–health interactions, Global health security

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