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

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:
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
Causality in Epidemiology01:21

Causality in Epidemiology

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...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:

You might also read

Related Articles

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

Sort by
Same author

Using covariance of node states to design early warning signals for network dynamics.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Energy landscape analysis based on the Ising model: Tutorial review.

PLOS complex systems·2026
Same author

Temporality modulates the effect of network heterogeneity on cooperation fixation.

Nature communications·2026
Same author

Detecting and forecasting tipping points from sample variance alone.

PNAS nexus·2026
Same author

Community structure unveils the path multiplicity in complex networks.

Nature communications·2026
Same author

Genomes from 117 vertebrate species reveal rapidly evolving segmental-duplication landscapes.

Genome biology and evolution·2026

Related Experiment Video

Updated: May 13, 2026

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

Predicting and controlling infectious disease epidemics using temporal networks.

Naoki Masuda1, Petter Holme

  • 1Department of Mathematical Informatics, The University of Tokyo 7-3-1 Hongo Bunkyo, Tokyo 113-8656 Japan.

F1000Prime Reports
|March 21, 2013
PubMed
Summary
This summary is machine-generated.

Recent advances in data collection enable the study of infectious disease spread using temporal social networks. This review explores network epidemiology tools for analyzing dynamic contact data to improve epidemic forecasting and prevention.

Related Experiment Videos

Last Updated: May 13, 2026

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

Area of Science:

  • Network epidemiology
  • Epidemic modeling
  • Data science

Background:

  • Infectious diseases spread through social networks in populations.
  • Advancements in data recording and analysis yield extensive time-stamped social contact data.
  • Traditional network epidemiology often treats networks as static, overlooking temporal dynamics.

Purpose of the Study:

  • To review recent findings in network epidemiology utilizing temporal network data.
  • To highlight the importance of time-stamped social contact data for understanding epidemic dynamics.
  • To discuss the need for advanced analytical tools for temporal network analysis in epidemiology.

Main Methods:

  • Review of recent literature on network epidemiology and temporal network analysis.
  • Analysis of time-stamped social contact data to capture network dynamics.
  • Exploration of computational tools for modeling epidemic spread on dynamic networks.

Main Results:

  • Temporal network data provides a more realistic representation of social interactions relevant to disease transmission.
  • Dynamic network models offer improved insights into epidemic spreading patterns compared to static models.
  • Recent studies demonstrate the potential of temporal network analysis for enhanced epidemic forecasting.

Conclusions:

  • Temporal network data is crucial for advancing the field of network epidemiology.
  • Development of sophisticated analytical tools is necessary to fully leverage dynamic network data for epidemic control.
  • Future research should focus on integrating temporal network analysis into practical epidemic prevention and response strategies.