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Reconstructing historic and modern potato late blight outbreaks using text analytics.

Ariel Saffer¹, Laura Tateosian¹, Amanda C Saville²

¹Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.

Scientific Reports

|February 15, 2024

Summary

Text analytics of historical records and modern social media reveal the spread and management of Phytophthora infestans, the pathogen causing late blight disease. This research helps track pandemics using unstructured data.

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Area of Science:

Plant pathology
Computational linguistics
Digital humanities

Background:

Phytophthora infestans, the pathogen responsible for late blight, has caused significant crop losses and historical famines.
Archival and modern documents contain valuable information on disease outbreaks, but this data is often unstructured and inaccessible.
Understanding the historical spread and management of plant diseases is crucial for addressing current and future food security challenges.

Purpose of the Study:

To utilize text analytics on historical reports (1843-1845) to map early US late blight outbreaks.
To characterize historical theories regarding the pathogen's origin and control strategies.
To develop modern late blight intensity maps using social media data (Twitter).

Main Methods:

Text analytics applied to unstructured historical documents (1843-1845).
Mapping of disease spread across US states and Canadian provinces.
Analysis of Twitter feeds for contemporary late blight discussions and intensity mapping.
Topic modeling to identify themes in historical and modern data.

Main Results:

The study mapped the spread of late blight from 5 to 17 states/provinces in the US and Canada between 1843-1845.
Historical documents discussed crop losses, potential Andean origins, and various control methods.
Modern Twitter data provided near-global and local disease observations, with topic modeling revealing information on disease, research, and outbreaks.

Conclusions:

Text analytics and social media mapping are powerful tools for exploring and visualizing historical and current pandemics.
This approach enhances the accessibility of unstructured data for tracking plant diseases.
The methodology can aid researchers in understanding disease dynamics and informing food security strategies.