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Global disease monitoring and forecasting with Wikipedia.

Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R - PLoS Comput. Biol. (2014)

Bottom Line: 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 r2 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.

View Article: PubMed Central - PubMed

Affiliation: Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

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 r2 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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Related in: MedlinePlus

Nowcast attempts where the model was unable to capture a meaningful pattern in official data.
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pcbi-1003892-g003: Nowcast attempts where the model was unable to capture a meaningful pattern in official data.

Mentions: Figure 3 illustrates the three contexts where the model was not effective because, we suspect, it was not able to discern meaningful patterns in the official data. These suggest a few patterns that models might have difficulty with:


Global disease monitoring and forecasting with Wikipedia.

Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R - PLoS Comput. Biol. (2014)

Nowcast attempts where the model was unable to capture a meaningful pattern in official data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231164&req=5

pcbi-1003892-g003: Nowcast attempts where the model was unable to capture a meaningful pattern in official data.
Mentions: Figure 3 illustrates the three contexts where the model was not effective because, we suspect, it was not able to discern meaningful patterns in the official data. These suggest a few patterns that models might have difficulty with:

Bottom Line: 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 r2 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.

View Article: PubMed Central - PubMed

Affiliation: Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

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 r2 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.

Show MeSH
Related in: MedlinePlus