Public health surveillance involves the detection and response to health threats. Conventional techniques rely on manual collection and reporting and tend to be extremely resource-intensive and reactive in nature. In this paper, an AI-based surveillance system has been developed that detects, in real-time, new trends and outbreaks from large volumes of health data and supports public health officials with timely and actionable insights in making informed decisions for protecting public health.
Week 1-2: Initial Planning and Requirement Analysis
Identify project objectives, scope, and high-level requirements
Collect sufficient data and resources Week 3-4: Design Phase
System architecture and data flow design
Wireframes and mockups for user interface Week 5-6: Collection and Integration
Design data sources and develop data processing pipelines
Ensure the quality and consistency of the data Week 7-8: Development of Artificial Intelligence Models
Implement AI models that analyze health data to predict trends
Train and validate models on collected data Week 9-10: Interface Development
Design the interface for use by the public health official and make it user-friendly and accessible.
Week 11-12: Testing and Refinement Phase
System tests for functionality and usability
Refine the system to meet performance metrics and user feedback
Compilation of final project report and documentation
Individual presentations of student reports
