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.

介绍了一个包含 1508 个样本的医学警报新闻数据集，其中有 30 个流行病学相关问题，提供了几个基准任务，包括命名实体识别（NER）、问题回答（QA）和事件提取（EE），是流行病学家和 NLP 研究人员的重要资源。

BAND：生物医学警报新闻数据集

BAND: Biomedical Alert News Dataset

We present "interoperability" as a guiding framework for statistical
modelling to assist policy makers asking multiple questions using diverse
datasets in the face of an evolving pandemic response. Interoperability
provides an important set of principles for future pandemic preparedness,
through the joint design and deployment of adaptable systems of statistical
models for disease surveillance using probabilistic reasoning. We illustrate
this through case studies for inferring spatial-temporal coronavirus disease
2019 (COVID-19) prevalence and reproduction numbers in England.

提出以 "互操作性" 为指导框架的统计建模，以协助政策制定者在面对不断演变的疫情应对中使用多样化数据询问多个问题。通过概率推理，我们设计和部署适应性统计模型系统，从而为未来的疫情准备工作提供重要的原则，具有疾病监测的功能。文中举例说明了在英格兰推断时空 COVID-19 预 valence 和再生数的情况。

流行病应对中统计模型的互操作性原则与现实

Interoperability of statistical models in pandemic preparedness:  principles and reality

Traditional disease surveillance can be augmented with a wide variety of
real-time sources such as, news and social media. However, these sources are in
general unstructured and, construction of surveillance tools such as
taxonomical correlations and trace mapping involves considerable human
supervision. In this paper, we motivate a disease vocabulary driven word2vec
model (Dis2Vec) to model diseases and constituent attributes as word embeddings
from the HealthMap news corpus. We use these word embeddings to automatically
create disease taxonomies and evaluate our model against corresponding human
annotated taxonomies. We compare our model accuracies against several
state-of-the art word2vec methods. Our results demonstrate that Dis2Vec
outperforms traditional distributed vector representations in its ability to
faithfully capture taxonomical attributes across different class of diseases
such as endemic, emerging and rare.

本文介绍了一种基于疾病词汇的 word2vec 模型（Dis2Vec），用于自动创建疾病分类法，以便更准确地进行疾病监测。结果表明，Dis2Vec 比传统方法更能准确地捕捉不同类别疾病的分类属性，并且性能更好。