Foodborne Illness Detection

Foodborne illness detection research focuses on rapidly and accurately identifying outbreaks using diverse data sources, aiming to minimize their impact on public health. Current efforts leverage machine learning, particularly deep learning models and ensemble methods like random forests, to analyze social media data (like tweets) and epidemiological case counts, often addressing challenges like class imbalance and limited labeled data through techniques such as transfer learning and crowdsourced labeling. These advancements enable faster outbreak detection, potentially improving public health responses and reducing economic losses associated with foodborne illnesses.