  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.
