  The design of models that govern diseases in population is commonly built on
information and data gathered from past outbreaks. However, epidemic outbreaks
are never captured in statistical data alone but are communicated by
narratives, supported by empirical observations. Outbreak reports discuss
correlations between populations, locations and the disease to infer insights
into causes, vectors and potential interventions. The problem with these
narratives is usually the lack of consistent structure or strong conventions,
which prohibit their formal analysis in larger corpora. Our interdisciplinary
research investigates more than 100 reports from the third plague pandemic
(1894-1952) evaluating ways of building a corpus to extract and structure this
narrative information through text mining and manual annotation. In this paper
we discuss the progress of our ongoing exploratory project, how we enhance
optical character recognition (OCR) methods to improve text capture, our
approach to structure the narratives and identify relevant entities in the
reports. The structured corpus is made available via Solr enabling search and
analysis across the whole collection for future research dedicated, for
example, to the identification of concepts. We show preliminary visualisations
of the characteristics of causation and differences with respect to gender as a
result of syntactic-category-dependent corpus statistics. Our goal is to
develop structured accounts of some of the most significant concepts that were
used to understand the epidemiology of the third plague pandemic around the
globe. The corpus enables researchers to analyse the reports collectively
allowing for deep insights into the global epidemiological consideration of
plague in the early twentieth century.
