Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mapping the landscape of individual-based models for respiratory pathogen transmission in the pandemic and post-pandemic era (2020-2024): A systematic review.

Epidemics·2026
Same author

Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic.

Scientific data·2026
Same author

A disease-agnostic approach to ensemble learning for infectious disease forecasting.

Nature communications·2026
Same author

When to vaccinate for seasonal influenza: check the peak forecast.

BMC public health·2025
Same author

Simulating nationwide coupled disease and fear spread in an agent-based model.

Scientific reports·2025
Same author

Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.

Journal of mathematical biology·2025
Same journal

Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2026
Same journal

Characterizing Online Activities Contributing to Suicide Mortality among Youth.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2026
Same journal

Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2026
Same journal

Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2025
Same journal

Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2025
Same journal

Reliability Analysis of Psychological Concept Extraction and Classification in User-penned Text.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media·2025
See all related articles

Related Experiment Video

Updated: Feb 26, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.2K

Eliciting Disease Data from Wikipedia Articles.

Geoffrey Fairchild1, Sara Y Del Valle1, Lalindra De Silva2

  • 1Los Alamos National Laboratory, Defense Systems & Analysis Division, Los Alamos, New Mexico, USA.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media
|July 20, 2017
PubMed
Summary
This summary is machine-generated.

This study shows Wikipedia can track disease outbreaks in near real-time. A trained system extracts case counts from Wikipedia, creating a valuable data repository for public health research and policy.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.6K

Related Experiment Videos

Last Updated: Feb 26, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.6K

Area of Science:

  • Public Health
  • Computational Epidemiology
  • Information Science

Background:

  • Traditional disease surveillance faces challenges like reporting delays and outdated technology.
  • Internet-based systems offer near real-time data but often lack comprehensive repositories.
  • Existing systems primarily focus on monitoring, not data storage for policy or research.

Purpose of the Study:

  • To explore Wikipedia as a source for near real-time disease surveillance data.
  • To develop a method for extracting epidemiological data from Wikipedia articles.
  • To establish a community-driven, open-source system for emerging disease detection and data repository.

Main Methods:

  • Analysis of Wikipedia article content for epidemiological data.
  • Training a named-entity recognizer to identify and tag case, death, and hospitalization counts.
  • Case study using the 2014 West African Ebola virus disease epidemic Wikipedia article.

Main Results:

  • A named-entity recognizer achieved an F1 score of 0.753 for extracting epidemiological counts.
  • Detailed time-series data from the Ebola epidemic article closely aligned with ground truth data.
  • Wikipedia content provides consistently updated, verifiable epidemiological information.

Conclusions:

  • Wikipedia can serve as a valuable source for emerging disease surveillance.
  • The proposed method enables the creation of a community-driven, open-source disease detection and repository system.
  • This approach addresses the gap in data repositories for policymakers and researchers.