Infectious disease outbreaks continue to pose a significant threat to human
health and well-being. To improve disease surveillance and understanding of
disease spread, several surveillance systems have been developed to monitor
daily news alerts and social media. However, existing systems lack thorough
epidemiological analysis in relation to corresponding alerts or news, largely
due to the scarcity of well-annotated reports data. To address this gap, we
introduce the Biomedical Alert News Dataset (BAND), which includes 1,508
samples from existing reported news articles, open emails, and alerts, as well
as 30 epidemiology-related questions. These questions necessitate the model's
expert reasoning abilities, thereby offering valuable insights into the
outbreak of the disease. The BAND dataset brings new challenges to the NLP
world, requiring better disguise capability of the content and the ability to
infer important information. We provide several benchmark tasks, including
Named Entity Recognition (NER), Question Answering (QA), and Event Extraction
(EE), to show how existing models are capable of handling these tasks in the
epidemiology domain. To the best of our knowledge, the BAND corpus is the
largest corpus of well-annotated biomedical outbreak alert news with
elaborately designed questions, making it a valuable resource for
epidemiologists and NLP researchers alike