Data Science Approaches to Infectious Disease Surveillance

This paper investigates how advanced data science techniques such as machine learning, network modelling, and big data analytics are transforming infectious disease surveillance. It presents case studies including COVID-19 and malaria to illustrate how integrating data from clinical, environmental, and social sources enhances outbreak prediction, detection, and response strategies. The authors argue for the incorporation of real-time data analytics into public health systems to build resilient surveillance mechanisms that can adapt to emerging global health threats.