Global disease monitoring and forecasting with Wikipedia

Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

doi:10.1371/journal.pcbi.1003892

Title: Global disease monitoring and forecasting with Wikipedia

Journal Article · Thu Nov 13 00:00:00 EST 2014 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1003892· OSTI ID:1214710

Generous, Nicholas ^[1]; Fairchild, Geoffrey ^[1]; Deshpande, Alina ^[1]; Del Valle, Sara Y. ^[1]; Priedhorsky, Reid ^[1]; Salathé, Marcel ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1214710

Journal Information:: PLoS Computational Biology (Online), Vol. 10, Issue 11; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 95 works

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Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus Daughton, Ashlynn R.; Paul, Michael J. Journal of Medical Internet Research, Vol. 21, Issue 5 https://doi.org/10.2196/13090	journal	January 2019
The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review Barros, Joana M.; Duggan, Jim; Rebholz-Schuhmann, Dietrich Journal of Medical Internet Research, Vol. 22, Issue 3 https://doi.org/10.2196/13680	journal	January 2020
Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language Heilman, James M.; West, Andrew G. Journal of Medical Internet Research, Vol. 17, Issue 3 https://doi.org/10.2196/jmir.4069	journal	January 2015
Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis Sharpe, J. Danielle; Hopkins, Richard S.; Cook, Robert L. JMIR Public Health and Surveillance, Vol. 2, Issue 2 https://doi.org/10.2196/publichealth.5901	journal	January 2016
Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System Baltrusaitis, Kristin; Santillana, Mauricio; Crawley, Adam W. JMIR Public Health and Surveillance, Vol. 3, Issue 2 https://doi.org/10.2196/publichealth.7304	journal	January 2017
Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology Meyers, Lindsay; Ginocchio, Christine C.; Faucett, Aimie N. JMIR Public Health and Surveillance, Vol. 4, Issue 3 https://doi.org/10.2196/publichealth.9876	journal	January 2018
Social Monitoring for Public Health Paul, Michael J.; Dredze, Mark Synthesis Lectures on Information Concepts, Retrieval, and Services, Vol. 9, Issue 5 https://doi.org/10.2200/s00791ed1v01y201707icr060	journal	August 2017
Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization Musa, Ibrahim; Park, Hyun; Munkhdalai, Lkhagvadorj Sustainability, Vol. 10, Issue 10 https://doi.org/10.3390/su10103414	journal	September 2018
Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment Bartlow, Andrew W.; Manore, Carrie; Xu, Chonggang Veterinary Sciences, Vol. 6, Issue 2 https://doi.org/10.3390/vetsci6020040	journal	May 2019
Evolution of Wikipedia’s medical content: past, present and future Shafee, Thomas; Masukume, Gwinyai; Kipersztok, Lisa La Trobe https://doi.org/10.26181/5ff3fdd660284	text	January 2021
Forecasting the 2013--2014 Influenza Season using Wikipedia Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid arXiv https://doi.org/10.48550/arxiv.1410.7716	text	January 2014
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance Santillana, Mauricio; Nguyen, Andre T.; Dredze, Mark arXiv https://doi.org/10.48550/arxiv.1508.06941	text	January 2015
Using internet search data to predict new HIV diagnoses in China: a modelling study Zhang, Qingpeng; Chai, Yi; Li, Xiaoming BMJ Open, Vol. 8, Issue 10 https://doi.org/10.1136/bmjopen-2017-018335	journal	October 2018
Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts Deiner, Michael S.; McLeod, Stephen D.; Chodosh, James Investigative Opthalmology & Visual Science, Vol. 59, Issue 2 https://doi.org/10.1167/iovs.17-22818	journal	February 2018
Situating Wikipedia as a health information resource in various contexts: A scoping review Smith, Denise A. PLOS ONE, Vol. 15, Issue 2 https://doi.org/10.1371/journal.pone.0228786	journal	February 2020
Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology Myers, Lindsay; Ginocchio, Christine. C.; Faucett, Aimie N. JMIR Public Health and Surveillance, Vol. 4, Issue 3 https://doi.org/10.2196/preprints.9876.a	journal	April 2018
Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies Park, Hyeoun-Ae; Jung, Hyesil; On, Jeongah Healthcare Informatics Research, Vol. 24, Issue 4 https://doi.org/10.4258/hir.2018.24.4.253	journal	January 2018
Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data Albinati, Julio; Meira, Wagner; Pappa, Gisele L. arXiv https://doi.org/10.48550/arxiv.1705.07879	preprint	January 2017
Epidemiological data challenges: planning for a more robust future through data standards Fairchild, Geoffrey; Tasseff, Byron; Khalsa, Hari arXiv https://doi.org/10.48550/arxiv.1805.00445	text	January 2018
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data Wang, Zijian; Hale, Scott A.; Adelani, David arXiv https://doi.org/10.48550/arxiv.1905.05961	text	January 2019
Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia Gozzi, Nicolò; Tizzani, Michele; Starnini, Michele arXiv https://doi.org/10.48550/arxiv.2006.06446	preprint	January 2020
Does the blue bird get the flu? using Twitter for flu surveillance Servan, Grüninger, University of Zurich https://doi.org/10.5167/uzh-152442	text	January 2017
Design Choices for Automated Disease Surveillance in the Social Web Magumba, Mark Abraham; Nabende, Peter; Mwebaze, Ernest Online Journal of Public Health Informatics, Vol. 10, Issue 2 https://doi.org/10.5210/ojphi.v10i2.9312	journal	September 2018

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59 BASIC BIOLOGICAL SCIENCES
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internet
infectious disease surveillance
machine learning algorithims

Title: Global disease monitoring and forecasting with Wikipedia

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