Overview
The OEC-2025 AlertME is a comprehensive crisis communication platform designed to enhance emergency response capabilities across Canada. This innovative system empowers users to report real-time incidents and visualize ongoing crises, including virus outbreaks, natural disasters, and other emergency situations that require immediate attention and coordinated response efforts.
At its core, the platform leverages advanced geographic data analysis to support future emergency planning initiatives. The system's most distinctive feature is its custom-built predictive generative AI model, which processes historical incident data to generate actionable insights for emergency management teams and public health officials.
The AI model was trained on a comprehensive dataset of reported COVID-19 cases throughout Ontario, incorporating critical temporal and spatial data points including report dates, precise latitude and longitude coordinates, and case severity metrics. Through sophisticated machine learning algorithms, the model analyzes patterns in disease transmission and geographic spread to extrapolate future trends.
By generating predictions for the upcoming year, the system enables proactive emergency management strategies. This predictive capability allows health authorities and emergency responders to implement preventative measures with unprecedented precision, potentially saving lives through early intervention and resource allocation based on data-driven forecasts of disease spread patterns.
Current Status
The OEC-2025 AlertME project was developed as a competition entry and is currently not in active development. While the concept demonstrated significant potential for real-world emergency management applications, the project timeline was constrained by the competition format and deliverable requirements.
During development, my primary responsibility involved configuring and implementing the MapBox integration for geographic visualization and mapping functionality. However, I discovered that my interests and strengths aligned more closely with the machine learning aspects of the project, particularly the model training and predictive analytics components.
This experience highlighted valuable insights about project role allocation and personal technical preferences. While the mapping implementation was successful, the opportunity to work more extensively on the AI model development and training processes would have provided greater learning value and better utilized my technical interests in machine learning and data science.
Despite not being in active development, the project serves as a proof-of-concept for integrating predictive AI models with geographic information systems for emergency management applications. The foundational work completed could potentially be expanded upon in future projects or real-world implementations.