Cloud-Enabled AI System for Early Epidemic Surveillance

Abstract

With the epidemic spreading at such a fast pace, it is imperious that early detection measures should be in place as quickly as possible. In this paper, an AI-driven epidemic surveillance framework in the cloud is proposed for the early detection of emerging infectious disease outbreaks and their real-time mapping. Based on the AI and cloud-based big data processing technologies, the system can collect a wealth of data from multiple sources like hospitals, wearable health devices, public health records and environmental sensors to build an integrated platform for real-time epidemic surveillance and prediction.First, the paper details how traditional epidemic surveillance systems are likely to be limited in scope because of lagged reports of cases, diseases and data silos resulting from their difficulty in predicting disease outbreaks. This framework seeks to overcome these issues by combining AI algorithms (such as machine learning models) with the cloud infrastructure in order to process real-time health data streams. By analyzing continuously-collected data, the system is able to recognize abnormal health-related patterns that may mark the eyes of an epidemic. By training AI models on historical disease data, correlations, trends and anomalies leading to the emergence of infectious diseases can be more easily identified which then results in faster and more accurate detection.