GLOBAL HEALTH INTELLIGENCE PLATFORMS DATA SCIENCE INFRASTRUCTURE FOR REAL-TIME EPIDEMIOLOGICAL SURVEILLANCE AND PANDEMIC FORECASTING

Abstract

The increasing rate of infectious disease outbreak and globalization has created the necessity to suggest new sophisticated surveillance and monitoring systems with the ability to monitor the situation in real-time and provide accurate forecasting of a pandemic. This paper describes how the surveillance of infectious diseases is transformed by combining artificial intelligence (AI), digital epidemiology, and big data infrastructure. It initially examines the causes of inefficiencies in the conventional disease surveillance systems, such as poor stakeholder participation, poor use of infrastructure, regulatory barriers, and poor awareness of policymakers. The paper next explains the application of AI in the detection of outbreaks with a focus on predictive analytics, geospatial maps, and natural language processing in the earliest identification of disease trends. Moreover, the shift to digital epidemiology based on classical epidemiology is discussed, and the opportunities and challenges related to the large-scale digital sources of data are outlined. A data science infrastructure of real-time surveillance, such as healthcare analytics, big data processing frameworks, and a layered architecture aimed at dealing with heterogeneous health data is also provided in the paper. Lastly, the paper will discuss the significance of diverse data sources in forecasting an epidemic and outline future prospects, including AI-driven analytics, secure data sharing with blockchain, and global collaboration efforts. Development of new policy-guided collaboration with advanced data science technologies can help improve global preparedness and response to up-emerging infectious diseases threats by a significant margin