250 - Automating QGIS maps: Using Python to visualize mosquito surveillance data.

Abstract: The Mosquito Research and Control Unit (MRCU) in the Cayman Islands manages extensive entomological surveillance data, including mosquito trap counts, rainfall, tide levels, and inspection records since 1965. From 1985, this data is stored in Microsoft Access, a relational database that supports tabular data management but lacks spatial visualization capabilities essential for identifying geographic patterns and informing mosquito control strategies. To overcome this limitation, a custom plugin was developed using Python and PyQt5 for integration with QGIS, a free and open-source Geographic Information System. The plugin automates the import, export, and visualization of surveillance data, transforming complex workflows into a streamlined, user-friendly interface. It connects directly to Access databases, applies meaningful styling to map layers, and enables interactive mapping and charting without requiring advanced GIS expertise. The solution leverages Python’s ecosystem including PyODBC for database connectivity, Matplotlib for data visualization, and the QGIS API for spatial rendering to support robust data handling and intuitive graphical interaction. By integrating multiple datasets into a unified QGIS dashboard, the tool empowers MRCU’s staff to visualize patterns, such as mosquito population fluctuations, identify hotspots, and make informed decisions quickly. The platform allows users to see patterns on a map canvas rather than reading numbers across multiple tables. This approach enhances operational efficiency and supports proactive vector control efforts. It demonstrates how open-source technologies like QGIS, combined with automation and scripting, can modernize public health data workflows and make spatial intelligence accessible to a broader range of users.