ArticlePLoS computational biology2014
Global disease monitoring and forecasting with Wikipedia.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Rethinking Dengue Preparedness in the Era of Climate Change, Urbanisation, and Digital Health: A Structured Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- AI-Enabled Diagnostic Prediction within Electronic Health Records to Enhance Biosurveillance and Early Outbreak Detection.Research square · 2025Article
- AI-Enabled Diagnostic Prediction within Electronic Health Records to Enhance Biosurveillance and Early Outbreak Detection.medRxiv : the preprint server for health sciences · 2025Article
- Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database.JMIR infodemiology · 2024Article
- Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study.Journal of medical Internet research · 2023Article
- Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information.Communications medicine · 2023Article
- What's hot and what's not in lay psychology: Wikipedia's most-viewed articles.Current psychology (New Brunswick, N.J.) · 2022Article
- Surveillance of emerging infectious diseases for biosecurity.Science China. Life sciences · 2022Review
- Query-based-learning mortality-related decoders for the developed island economy.Scientific reports · 2022Article
- Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic.GigaScience · 2022Article
- Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates.PloS one · 2022Article
- Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.Frontiers in public health · 2022Article
- JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook.Journal of urban economics · 2022Article
- Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.PLoS medicine · 2021Article
- Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread.NPJ digital medicine · 2021Article
- An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.Science advances · 2021Article
- Correlation between flu and Wikipedia's pages visualization.Acta bio-medica : Atenei Parmensis · 2021Article
- Survey data and human computation for improved flu tracking.Nature communications · 2021Article
- A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data.PloS one · 2021Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.