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

Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)