Global Disease Monitoring and Forecasting with Wikipedia: e1003892

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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Bibliographic Details
Published in:PLoS computational biology 2014-11, Vol.10 (11)
Main Authors: Generous, Nicholas, Fairchild, Geoffrey, Deshpande, Alina, Valle, Sara YDel, Priedhorsky, Reid
Format: Article
Language:English
Subjects:
Data sources
Economics
Encyclopaedias
Encyclopedias
Experiments
Forecasting
Industrialized nations
Infectious diseases
Internet access
Laboratories
Mathematical models
Monitoring
Mortality
Peer review
Risk
Searching
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Summary: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.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003892