Jove
Visualize
Contact Us

Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

853
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
853
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

74
Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
74
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

779
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
779
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K

You might also read

Related Articles

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

Sort by
Same author

Pediatric High-Grade Gliomas and Cancer Predisposition Syndromes: A Retrospective Study.

HGG advances·2026
Same author

Mapping risk communication practices in public health emergencies: a scoping review and comparison with Italian regional pandemic plans.

BMC public health·2026
Same author

Validation of a large language model as a decision tool for drug interaction in prostate cancer: A comparative study against UpToDate. Meet-URO 5/25 - GENIE study.

Journal of geriatric oncology·2026
Same author

Seamless monitoring of stress levels leveraging a foundational model for time sequences.

Artificial intelligence in medicine·2026
Same author

Social media insights on the introduction of RSV immunoprophylaxis in Italy.

Human vaccines & immunotherapeutics·2025
Same author

Italy's NITAG shift: implications for public trust in vaccines.

The Lancet regional health. Europe·2025
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 Experiment Video

Updated: May 2, 2026

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

24.2K

Twitter mining for fine-grained syndromic surveillance.

Paola Velardi1, Giovanni Stilo1, Alberto E Tozzi2

  • 1Department of Computer Science, University of Rome "Sapienza", via Salaria 113, 00198 Rome, Italy.

Artificial Intelligence in Medicine
|March 12, 2014
PubMed
Summary

This study introduces a novel method for real-time epidemic detection using Twitter data. By analyzing everyday language and symptoms, it accurately tracks disease trends, outperforming existing tools.

Keywords:
Micro-blog miningPatient's language learningSyndromic surveillanceTerminology clusteringTwitter mining

More Related Videos

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.5K
Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

7.7K

Related Experiment Videos

Last Updated: May 2, 2026

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

24.2K
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.5K
Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens
09:08

Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens

Published on: September 12, 2016

7.7K

Area of Science:

  • Digital epidemiology
  • Public health surveillance
  • Computational social science

Background:

  • Internet data offers real-time insights for syndromic surveillance.
  • Analyzing user-generated content requires understanding everyday language, not just medical jargon.

Purpose of the Study:

  • To develop and present a methodology for early epidemic detection and analysis by mining Twitter messages.
  • To improve the reliability of tracing patient messages by learning informal medical language and adopting a symptom-driven analysis.

Main Methods:

  • Developed an algorithm to automatically learn patient terminology for health conditions.
  • Implemented a Twitter monitoring system to analyze symptom presence and combinations in tweets.
  • Utilized physician knowledge to link patient language to medical concepts.

Main Results:

  • The algorithm effectively learns expressions for health conditions, enhancing detection of health-related concepts.
  • The Twitter monitoring system allows for fine-grained classification of tweets, distinguishing between similar conditions like influenza-like illness and the common cold.
  • The approach demonstrated high correlation with traditional flu surveillance data.

Conclusions:

  • The proposed method provides a flexible and sensitive tool for epidemic surveillance.
  • It offers advantages over search-volume-based tools like Google Flu by being less susceptible to changes in user search behavior.