Federated AI Approaches in Pandemic Outbreak Forecasting

Abstract

The recent global pandemics, such as COVID-19, have underscored the urgent need for accurate and timely outbreak forecasting to mitigate the impacts of infectious diseases. Traditional methods of forecasting disease outbreaks often struggle with issues such as data privacy concerns, regional disparities in healthcare resources, and the limitations of centralized data processing. Federated AI, a decentralized approach to machine learning, has emerged as a potential solution to these challenges. By allowing models to be trained on decentralized datasets without transferring sensitive health data, federated AI ensures privacy while improving the accuracy and scalability of pandemic forecasting models. This paper explores the use of federated AI approaches in pandemic outbreak forecasting, highlighting how it can address key issues such as data accessibility, security, and collaboration across borders. Through the aggregation of knowledge from diverse datasets, federated learning models can produce more accurate predictions of disease spread, healthcare system strain, and resource demands. Despite challenges like data heterogeneity, communication overhead, and regulatory barriers, federated AI holds promise in transforming global health response strategies. The paper discusses the practical applications of federated AI in past pandemics, such as COVID-19, and explores future innovations that could further improve the effectiveness of these models in forecasting and managing pandemic outbreaks. Ultimately, federated AI represents a transformative opportunity to enhance global health security and resilience in the face of future pandemics.