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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

324
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:
324
Causality in Epidemiology01:21

Causality in Epidemiology

1.2K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.2K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

708
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:
708
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

322
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...
322
Transmission-based Precautions I: Contact, Enteric, and Droplets01:17

Transmission-based Precautions I: Contact, Enteric, and Droplets

4.3K
Transmission-based precautions are for patients known to be infected or suspected to be infected or colonized with organisms that pose a significant risk to others. Some transmission-based precautions include contact, enteric, and droplet.
Contact Precautions:
Contact precautions are the measures taken to prevent the transmission of infectious agents, especially epidemiologically important microorganisms such as MRSA or influenza, primarily transmitted through direct or indirect contact with an...
4.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.0K

You might also read

Related Articles

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

Sort by
Same author

AI support for data scientists: An empirical study on workflow and alternative code recommendations.

Empirical software engineering·2025
Same author

Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions.

PloS one·2025
Same author

We need to understand the effect of narratives about generative AI.

Nature human behaviour·2024
Same author

Trolleys, crashes, and perception-a survey on how current autonomous vehicles debates invoke problematic expectations.

AI and ethics·2024
Same author

Active querying approach to epidemic source detection on contact networks.

Scientific reports·2023
Same author

Visualising data science workflows to support third-party notebook comprehension: an empirical study.

Empirical software engineering·2023
Same journal

Changes in patient-sharing patterns after oncologist departures in rural and urban settings: a Medicare cohort study.

Applied network science·2026
Same journal

Tunable network properties with Hamill and Gilbert's Social Circles generator.

Applied network science·2025
Same journal

Initialisation and network effects in decentralised federated learning.

Applied network science·2025
Same journal

The association of prescriber prominence in a shared-patient physician network with their patients receipt of and transitions between risky drug combinations.

Applied network science·2025
Same journal

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.

Applied network science·2025
Same journal

Navigation on temporal networks.

Applied network science·2025
See all related articles

Related Experiment Video

Updated: Nov 14, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.9K

Outbreak detection for temporal contact data.

Martin Sterchi1,2,3, Cristina Sarasua1, Rolf Grütter2

  • 1Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzerland.

Applied Network Science
|March 8, 2021
PubMed
Summary
This summary is machine-generated.

Optimally selecting monitoring nodes for epidemic spreading is crucial. Simple node selection methods, like those based on high contact numbers, perform comparably to complex strategies for outbreak detection.

Keywords:
Epidemic spreadingGreedy optimizationOutbreak detectionSubmodular functionsTemporal networks

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.9K

Related Experiment Videos

Last Updated: Nov 14, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.9K

Area of Science:

  • Network science
  • Epidemiology
  • Computational modeling

Background:

  • Epidemic spreading impacts society, extending beyond pathogens to information and computer viruses.
  • Monitoring networks are essential for early outbreak detection and mitigation.

Purpose of the Study:

  • To determine optimal node selection strategies for monitoring epidemic spreading in timestamped contact networks.
  • To evaluate detection likelihood, time to detection, and affected population across different optimization objectives.

Main Methods:

  • Utilized a greedy optimization approach adapted from information spreading models.
  • Applied the method to timestamped sexual contact and animal transport networks.
  • Simulated outbreak scenarios using a susceptible-infectious-recovered (SIR) model.

Main Results:

  • Heuristic node selection (e.g., high degree) performs comparably to greedy optimal methods.
  • Nodes optimized for past periods may not be optimal for future outbreak detection.
  • Detection performance improves with larger simulated outbreaks, which occur in highly connected network regions.

Conclusions:

  • Simple heuristics offer effective strategies for monitoring epidemic spreading.
  • Dynamic network conditions and seasonality influence optimal monitoring node selection.
  • Further research should explore realistic propagation models for scenario generation.