Global disease monitoring and forecasting with Wikipedia
Abstract
Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoringmore »
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1214710
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online); Journal Volume: 10; Journal Issue: 11; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; online encyclopedias; forecasting; influenza; disease surveillance; language; internet; infectious disease surveillance; machine learning algorithims
Citation Formats
Generous, Nicholas, Fairchild, Geoffrey, Deshpande, Alina, Del Valle, Sara Y., Priedhorsky, Reid, and Salathé, Marcel. Global disease monitoring and forecasting with Wikipedia. United States: N. p., 2014.
Web. doi:10.1371/journal.pcbi.1003892.
Generous, Nicholas, Fairchild, Geoffrey, Deshpande, Alina, Del Valle, Sara Y., Priedhorsky, Reid, & Salathé, Marcel. Global disease monitoring and forecasting with Wikipedia. United States. https://doi.org/10.1371/journal.pcbi.1003892
Generous, Nicholas, Fairchild, Geoffrey, Deshpande, Alina, Del Valle, Sara Y., Priedhorsky, Reid, and Salathé, Marcel. Thu .
"Global disease monitoring and forecasting with Wikipedia". United States. https://doi.org/10.1371/journal.pcbi.1003892. https://www.osti.gov/servlets/purl/1214710.
@article{osti_1214710,
title = {Global disease monitoring and forecasting with Wikipedia},
author = {Generous, Nicholas and Fairchild, Geoffrey and Deshpande, Alina and Del Valle, Sara Y. and Priedhorsky, Reid and Salathé, Marcel},
abstractNote = {Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.},
doi = {10.1371/journal.pcbi.1003892},
journal = {PLoS Computational Biology (Online)},
number = 11,
volume = 10,
place = {United States},
year = {Thu Nov 13 00:00:00 EST 2014},
month = {Thu Nov 13 00:00:00 EST 2014}
}
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Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
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Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia
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