Reconstructing historic and modern potato late blight outbreaks using text analytics. Saffer, A., Tateosian, L., Saville, A. C., Yang, Y., & Ristaino, J. B. Scientific Reports, 14(1):2523, February, 2024. Publisher: Nature Publishing GroupPaper doi abstract bibtex In 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known as Phytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.
@article{saffer_reconstructing_2024,
title = {Reconstructing historic and modern potato late blight outbreaks using text analytics},
volume = {14},
copyright = {2024 The Author(s)},
issn = {2045-2322},
url = {https://www.nature.com/articles/s41598-024-52870-2},
doi = {10.1038/s41598-024-52870-2},
abstract = {In 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known as Phytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.},
language = {en},
number = {1},
urldate = {2024-03-11},
journal = {Scientific Reports},
author = {Saffer, Ariel and Tateosian, Laura and Saville, Amanda C. and Yang, Yi-Peng and Ristaino, Jean B.},
month = feb,
year = {2024},
note = {Publisher: Nature Publishing Group},
keywords = {Political Boundaries},
pages = {2523},
}
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A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. 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