Machine Learning Approaches for Early Detection and Control of Emerging Infectious Diseases

Abstract

The rapid emergence and re-emergence of infectious diseases pose a significant threat to global public health, necessitating the development of advanced detection and control mechanisms. Traditional epidemiological approaches often rely on delayed reporting and limited data integration, resulting in slow response times and increased transmission risk. This study explores the application of machine learning techniques for the early detection and control of emerging infectious diseases. By integrating heterogeneous datasets, including clinical records, genomic data, environmental variables, and mobility patterns, the research develops predictive models capable of identifying outbreaks at an early stage. Algorithms such as logistic regression, random forest, support vector machines, and deep neural networks are evaluated for their effectiveness in disease prediction and classification. The results demonstrate that machine learning models significantly improve detection accuracy and response time compared to conventional methods. Furthermore, the study highlights the role of predictive analytics in guiding public health interventions, including resource allocation and containment strategies. The findings underscore the potential of machine learning in transforming infectious disease surveillance into a proactive, data-driven system capable of mitigating global health risks.