Traditional disease surveillance systems suffer from several disadvantages,
including reporting lags and antiquated technology, that have caused a movement
towards internet-based disease surveillance systems. Internet systems are
particularly attractive for disease outbreaks because they can provide data in
near real-time and can be verified by individuals around the globe. However,
most existing systems have focused on disease monitoring and do not provide a
data repository for policy makers or researchers. In order to fill this gap, we
analyzed Wikipedia article content.
We demonstrate how a named-entity recognizer can be trained to tag case
counts, death counts, and hospitalization counts in the article narrative that
achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola
virus disease epidemic article as a case study, that there are detailed time
series data that are consistently updated that closely align with ground truth
data.
We argue that Wikipedia can be used to create the first community-driven
open-source emerging disease detection, monitoring, and repository system.

运用维基百科文章内容，基于命名实体识别器，实现对病例，死亡和住院人数在文章叙述中的标记，并且利用 2014 年西非埃博拉病毒疾病流行文章为案例研究，来展示与真实数据高度一致的详细时间序列数据，从而提出维基百科可以被用于创建第一个以社区为驱动的开源新兴疾病检测、监测和库存系统的观点。