Mapping the plague through natural language processing

ElsevierVolume 41, December 2022, 100656EpidemicsAuthor links open overlay panelHighlights•

Natural language processing is a promising tool for the generation of epidemiological datasets.

Named entity recognition can be used to detect places with outbreaks of historical plague.

New data has been added to understand the spatio-temporal extent of the second plague pandemic in Eurasia.

Abstract

Pandemic diseases such as plague have produced a vast amount of literature providing information about the spatiotemporal extent, transmission, or countermeasures. However, the manual extraction of such information from running text is a tedious process, and much of this information remains locked into a narrative format. Natural Language processing (NLP) is a promising tool for the automated extraction of epidemiological data, and can facilitate the establishment of datasets. In this paper, we explore the utility of NLP to assist in the creation of a plague outbreak dataset. We produced a gold standard list of toponyms by manual annotation of a German plague treatise published by Sticker in 1908. We investigated the performance of five pre-trained NLP libraries (Google, Stanford CoreNLP, spaCy, germaNER and Geoparser) for the automated extraction of location data compared to the gold standard. Of all tested algorithms, spaCy performed best (sensitivity 0.92, F1 score 0.83), followed closely by Stanford CoreNLP (sensitivity 0.81, F1 score 0.87). Google NLP had a slightly lower performance (F1 score 0.72, sensitivity 0.78). Geoparser and germaNER had a poor sensitivity (0.41 and 0.61). We then evaluated how well automated geocoding services such as Google geocoding, Geonames and Geoparser located these outbreaks correctly. All geocoding services performed poorly – particularly for historical regions – and returned the correct GIS information only in 60.4%, 52.7% and 33.8% of all cases. Finally, we compared our newly digitized plague dataset to a re-digitized version of the plague treatise by Biraben and provide an update of the spatio-temporal extent of the second pandemic plague outbreaks. We conclude that NLP tools have their limitations, but they are potentially useful to accelerate the collection of data and the generation of a global plague outbreak database.

Keywords

Plague

Infectious diseases

Historical epidemiology

Outbreaks

Natural language processing

Machine learning

Data Availability

The R code and the digitized plague datasets are available in a public repository (Krauer and Schmid, 2021) (https://doi.org/10.5281/zenodo.6587267).

© 2022 The Authors. Published by Elsevier B.V.

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