  In this paper, we present a manually annotated corpus of 10,000 tweets
containing public reports of five COVID-19 events, including positive and
negative tests, deaths, denied access to testing, claimed cures and
preventions. We designed slot-filling questions for each event type and
annotated a total of 31 fine-grained slots, such as the location of events,
recent travel, and close contacts. We show that our corpus can support
fine-tuning BERT-based classifiers to automatically extract publicly reported
events and help track the spread of a new disease. We also demonstrate that, by
aggregating events extracted from millions of tweets, we achieve surprisingly
high precision when answering complex queries, such as "Which organizations
have employees that tested positive in Philadelphia?" We will release our
corpus (with user-information removed), automatic extraction models, and the
corresponding knowledge base to the research community.
