An Intelligent Disease Surveillance Data Feedback System

Amelia Taylor of Malawi University of Business and Applied Sciences in Malawi will employ Large Language Models (LLMs), including ChatGPT and MedPalm, to develop a tool to streamline the collection, analysis, and use of COVID-19 data. Collecting accurate and comprehensive data during a pandemic is critical for response efforts but the process is labor-intensive. During COVID-19 surveillance, there were also limited training materials available to explain specialized concepts for data collection to the multidisciplinary teams. To address this, they will leverage their experience in the operational aspects of COVID-19 data management in Malawi to develop an automated feedback tool that records high-quality, complete data while ensuring compatibility across diverse sources and destinations. Furthermore, together with frontline health workers and epidemiologists, they will create a knowledge map of symptoms and clinical terminologies to support clinicians, lab technicians and surveillance officers engaged in data collection.