Global Disease Monitoring and Forecasting with Wikipedia
Publication Date
November 13, 2014
Journal
PLOS Computational Biology
Authors
Nicholas Generous, Geoffrey Fairchild, Alina Deshpande, Sara Y. Del Valle, et al
Volume
10
Issue
11
Pages
e1003892
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003892
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003892
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25392913
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231164
Europe PMC
http://europepmc.org/abstract/MED/25392913
Web of Science
000345454400005
Scopus
84912131759
Mendeley
http://www.mendeley.com/research/global-disease-monitoring-forecasting-wikipedia-6
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Mendeley | Further Information

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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.", "link"=>"http://www.mendeley.com/research/global-disease-monitoring-forecasting-wikipedia-6", "reader_count"=>136, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>10, "Researcher"=>32, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1793395"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240698, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892", "stats"=>{"downloads"=>28, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_Disease_Monitoring_and_Forecasting_with_Wikipedia_/1240698", "title"=>"Global Disease Monitoring and Forecasting with Wikipedia", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793376"], "description"=>"<p>This table lists the transferability scores <i>r</i><sub>t</sub> for each tested pair of countries within a disease. Countries that did not share enough articles to compute a meaningful <i>r</i><sub>t</sub> are indicated with <i>n/a</i>.</p><p>Transferability scores <i>r</i><sub>t</sub> for paired models.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240679, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.t004", "stats"=>{"downloads"=>4, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transferability_scores_r_t_for_paired_models_/1240679", "title"=>"Transferability scores <i>r</i><sub>t</sub> for paired models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793375"], "description"=>"<p>This table summarizes the performance of our estimation models. For each disease and location, we list the subjective success/failure classification as well as model <i>r</i><sup>2</sup> at nowcasting (0-day forecast) and 7-, 14-, and 28-day forecasts. We also list the temporal offset in days of the best model (again, a positive offset indicates forecasting) along with that model's <i>r</i><sup>2</sup>.</p><p>Model performance summary.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240678, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.t003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_performance_summary_/1240678", "title"=>"Model performance summary.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793371"], "description"=>"<p>Nowcast attempts with poor performance due to unfavorable signal-to-noise ratio.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240674, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nowcast_attempts_with_poor_performance_due_to_unfavorable_signal_to_noise_ratio_/1240674", "title"=>"Nowcast attempts with poor performance due to unfavorable signal-to-noise ratio.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793366"], "description"=>"<p>This figure shows model <i>r</i><sup>2</sup> compared to temporal offset in days: positive offsets are forecasting, zero is nowcasting (marked with a dotted line), and negative offsets are anti-forecasting. As above, figures for the four successful contexts not included here are in the supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003892#pcbi.1003892.s001\" target=\"_blank\">data S1</a>.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240669, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Forecasting_effectiveness_for_selected_successful_models_/1240669", "title"=>"Forecasting effectiveness for selected successful models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793365"], "description"=>"<p>These graphs show official epidemiological data and nowcast model estimate (left Y axis) with traffic to the five most-correlated Wikipedia articles (right Y axis) over the 3 year study periods. The Wikipedia time series are individually self-normalized. Graphs for the four remaining successful contexts (dengue in Thailand, influenza in Japan, influenza in Thailand, and tuberculosis in Thailand) are included in the supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003892#pcbi.1003892.s001\" target=\"_blank\">data file S1</a>.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240668, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_successful_model_nowcasts_/1240668", "title"=>"Selected successful model nowcasts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793374"], "description"=>"<p>This table shows simplified models for influenza in two locations: Japan, where Japanese is spoken, and Thailand, where Thai is spoken. The Japanese model yielded correlations for Japanese versions of the articles “Fever”, “Chills”, “Headache”, and “Influenza” of 0.23, 0.59, −0.10, and 0.85, respectively. The Thai model yielded correlations of 0.21, 0.15, and 0.77 for “Fever”, “Headache”, and “Influenza”, respectively. Note that the article “Chills” is not currently present in the Thai Wikipedia. Therefore, the correlation vectors are and for the two languages. The meta-correlation, <i>r</i><sub>t</sub>, for these two vectors, which provides a gross estimate of how similar the models are, is 0.97. Extending this computation to the full models yields <i>r</i><sub>t</sub> = 0.81, as noted below in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003892#pcbi-1003892-t004\" target=\"_blank\">Table 4</a>.</p><p>Transferability <i>r</i><sub>t</sub> example.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240677, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.t002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transferability_r_t_example_/1240677", "title"=>"Transferability <i>r</i><sub>t</sub> example.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793373"], "description"=>"<p>This table lists the 7 diseases in 9 locations analyzed, for a total of 14 disease-location contexts. For each context, we list the language used as a location proxy, the inclusive start and end dates of analysis, the resolution of the disease incidence data, and one or more citations for those data.</p><p>Diseases-location contexts analyzed, with data sources.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240676, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.t001", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diseases_location_contexts_analyzed_with_data_sources_/1240676", "title"=>"Diseases-location contexts analyzed, with data sources.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-13 04:01:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1793370"], "description"=>"<p>Nowcast attempts where the model was unable to capture a meaningful pattern in official data.</p>", "links"=>[], "tags"=>["research agenda", "data feasibly support", "access logs", "Global Disease Monitoring", "technique", "search queries", "location", "Peer review", "forecasting system", "article selection procedure", "encyclopedia Wikipedia", "disease monitoring", "data source", "28 days", "challenge", "internet data", "model"], "article_id"=>1240673, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Nicholas Generous", "Geoffrey Fairchild", "Alina Deshpande", "Sara Y. Del Valle", "Reid Priedhorsky"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003892.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nowcast_attempts_where_the_model_was_unable_to_capture_a_meaningful_pattern_in_official_data_/1240673", "title"=>"Nowcast attempts where the model was unable to capture a meaningful pattern in official data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-13 04:01:22"}

PMC Usage Stats | Further Information

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