About the Project

The project aims to leverage Large Language Models (LLMs) to automate the

summarization of cholera data records, improving the accessibility and analysis of

outbreak-related information. Cholera remains a significant public health concern,

particularly in resource-limited settings where rapid response and data-driven

decision-making are critical. Traditional cholera surveillance and reporting systems generate

extensive datasets, often requiring significant manual effort to extract key insights.

This project will develop an LLM-based framework capable of processing unstructured and

structured cholera data, generating concise, informative summaries for health officials,

researchers, and policymakers. The model will be trained to identify key epidemiological

indicators, including case counts, mortality rates, geographical spread, and intervention

outcomes. By streamlining data interpretation, the system aims to enhance situational

awareness, facilitate early detection of outbreaks, and support timely decision-making.