The Intelligent Public Health Surveillance System: A Cutting-Edge Approach to Monitoring and Predicting Public Health Threats

Timely detection of public health outbreak is necessary due to interconnected world that allows us to communicate beyond borders. Current systems typically fail to bring together diverse data streams in real time, meaning fresh new health emergencies escalate more than necessary. Moreover, standard methods often fail to adapt to new diseases and new patterns in time to prevent a new crisis. To overcome these limitations, we propose the Intelligent Public Health Surveillance System (IPHSS) which will be an Artificial Intelligence (AI) based public health surveillance platform. The AI powered system collects and evaluates organized and disorganized information from hospitals, patient wearables and social media. It uses a variety of Machine Learning (ML) techniques to predict the time series of cases using Long Short-Term Memory (LSTM) and to detect outliers with Real-Time Incremental Support Vector Machines (RISVM). Besides, it also supports geospatial clustering for hotspot detection and adaptive algorithms that respond to changing patterns of new diseases. The major benefit of the system is its adaptability to unforeseen health trends and new data, unlike conventional systems. The system is designed to grow easily with the addition of different types of data. There are also plans to add strong Natural Language Processing (NLP). The ever-growing NLP can be used to analyze and process multilingual data. Furthermore, the adaptability of prediction modeling will also see improvements in the future. Through delivery of timely and actionable insights, IPHSS supports decisionmaking by public health agencies. This revolutionary method is a big jump to establishing a public health surveillance system that is active, intelligent, and globally adaptive.