  Since the start of COVID-19, several relevant corpora from various sources
are presented in the literature that contain millions of data points. While
these corpora are valuable in supporting many analyses on this specific
pandemic, researchers require additional benchmark corpora that contain other
epidemics to facilitate cross-epidemic pattern recognition and trend analysis
tasks. During our other efforts on COVID-19 related work, we discover very
little disease related corpora in the literature that are sizable and rich
enough to support such cross-epidemic analysis tasks. In this paper, we present
EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog
posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M
contains a subset of 26.2 millions tweets related to three general diseases,
namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets
of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti
Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola,
2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the
properties of the corpus with statistics of key terms and hashtags and trends
analysis for each subset. Finally, we demonstrate the value and impact that
EPIC30M could create through a discussion of multiple use cases of
cross-epidemic research topics that attract growing interest in recent years.
These use cases span multiple research areas, such as epidemiological modeling,
pattern recognition, natural language understanding and economical modeling.
