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Mapping the plague through natural language processing.

Fabienne Krauer1, Boris V Schmid1

  • 1Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, 0316 Oslo, Norway.

Epidemics
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

Natural Language Processing (NLP) tools show promise for automating plague outbreak data extraction. spaCy and Stanford CoreNLP libraries performed best in identifying locations, aiding global plague database creation.

Keywords:
Historical epidemiologyInfectious diseasesMachine learningNatural language processingOutbreaksPlague

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Area of Science:

  • Computational epidemiology
  • Digital humanities
  • Historical disease surveillance

Background:

  • Manual extraction of epidemiological data from historical texts is time-consuming.
  • Vast literature on past pandemics like plague contains valuable spatiotemporal and transmission data.
  • Automated data extraction can facilitate the creation of comprehensive disease outbreak datasets.

Purpose of the Study:

  • To evaluate the effectiveness of Natural Language Processing (NLP) libraries for extracting location data from historical plague texts.
  • To assess the performance of automated geocoding services for historical geographical regions.
  • To contribute to the development of a global plague outbreak database.

Main Methods:

  • Manual annotation of a German plague treatise to create a gold standard for toponyms.
  • Performance evaluation of five pre-trained NLP libraries (Google, Stanford CoreNLP, spaCy, germaNER, Geoparser) against the gold standard.
  • Assessment of automated geocoding services (Google geocoding, Geonames, Geoparser) for accuracy with historical data.

Main Results:

  • spaCy demonstrated the highest performance in location extraction (sensitivity 0.92, F1 score 0.83), followed by Stanford CoreNLP (F1 score 0.87).
  • Automated geocoding services showed poor performance, especially for historical regions, with correct GIS information retrieval ranging from 33.8% to 60.4%.
  • A newly digitized plague dataset was compared to a previous version, updating the spatio-temporal extent of second pandemic plague outbreaks.

Conclusions:

  • NLP tools offer a potentially valuable method to accelerate data collection for plague outbreak research.
  • Limitations exist in current NLP and geocoding technologies, particularly for historical data.
  • Further development of NLP tools can significantly aid in building global historical disease databases.