Using machine learning to predict disease outbreaks and enhance public health surveillance
Foluke Ekundayo
Abstract
Disease outbreaks pose significant challenges to public health systems, often requiring rapid response strategies to mitigate widespread health and economic impacts. Traditional methods of outbreak prediction and surveillance, while effective, often lack the capacity to process and analyse the vast quantities of heterogeneous data generated in modern healthcare ecosystems. Machine learning (ML) offers transformative potential in this domain, leveraging its ability to process large datasets, identify complex patterns, and provide real-time insights. By integrating diverse data sources such as electronic health records (EHRs), social media feeds, climate data, and genomic sequences, ML algorithms can predict disease outbreaks with unprecedented accuracy. Supervised learning models, for instance, have been successfully applied to forecast influenza trends, while unsupervised clustering techniques have been employed to detect anomalies indicative of emerging infectious diseases. Moreover, ML facilitates advanced public health surveillance by automating data processing pipelines, enhancing real-time monitoring capabilities, and enabling resource optimization for outbreak responses. Despite these advances, the adoption of ML in public health surveillance is not without challenges. Issues related to data privacy, ethical considerations, algorithm interpretability, and integration with existing public health infrastructures remain significant hurdles. Addressing these challenges requires a multidisciplinary approach, incorporating robust data governance frameworks, improved algorithm transparency, and collaborations between technology developers and public health stakeholders. This paper highlights the critical role of ML in transforming public health surveillance, focusing on its application in disease outbreak prediction. It underscores the importance of continued innovation, regulatory support, and ethical considerations in advancing ML-driven solutions for global health security.