197 - Streamlining Mosquito Arbovirus Surveillance: In-House Data Migration to Web-Based Software

Abstract: Effective mosquito arbovirus surveillance is essential for early detection and timely intervention in controlling vector-borne diseases. This project focused on the in-house migration of mass arbovirus testing data from traditional PDF-based reports to a more efficient web-based software platform, VectorSurv. Using Python, along with libraries such as pandas and pytesseract, data was extracted, cleaned, and transformed to meet VectorSurv's import standards. The process involved handling various data inconsistencies, automating data extraction with OCR, and employing pandas for rapid data cleaning and transformation. By automating the migration, the project significantly reduced manual data entry time and improved data accuracy by eliminating transcription errors.