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Global Disease Monitoring and Forecasting with Wikipedia

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 b...

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Autores principales: Generous, Nicholas, Fairchild, Geoffrey, Deshpande, Alina, Del Valle, Sara Y., Priedhorsky, Reid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231164/
https://www.ncbi.nlm.nih.gov/pubmed/25392913
http://dx.doi.org/10.1371/journal.pcbi.1003892
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author Generous, Nicholas
Fairchild, Geoffrey
Deshpande, Alina
Del Valle, Sara Y.
Priedhorsky, Reid
author_facet Generous, Nicholas
Fairchild, Geoffrey
Deshpande, Alina
Del Valle, Sara Y.
Priedhorsky, Reid
author_sort Generous, Nicholas
collection PubMed
description 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 [Image: see text] 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.
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spelling pubmed-42311642014-11-18 Global Disease Monitoring and Forecasting with Wikipedia Generous, Nicholas Fairchild, Geoffrey Deshpande, Alina Del Valle, Sara Y. Priedhorsky, Reid PLoS Comput Biol Research Article 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 [Image: see text] 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. Public Library of Science 2014-11-13 /pmc/articles/PMC4231164/ /pubmed/25392913 http://dx.doi.org/10.1371/journal.pcbi.1003892 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Generous, Nicholas
Fairchild, Geoffrey
Deshpande, Alina
Del Valle, Sara Y.
Priedhorsky, Reid
Global Disease Monitoring and Forecasting with Wikipedia
title Global Disease Monitoring and Forecasting with Wikipedia
title_full Global Disease Monitoring and Forecasting with Wikipedia
title_fullStr Global Disease Monitoring and Forecasting with Wikipedia
title_full_unstemmed Global Disease Monitoring and Forecasting with Wikipedia
title_short Global Disease Monitoring and Forecasting with Wikipedia
title_sort global disease monitoring and forecasting with wikipedia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231164/
https://www.ncbi.nlm.nih.gov/pubmed/25392913
http://dx.doi.org/10.1371/journal.pcbi.1003892
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