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

225
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:
225
Causality in Epidemiology01:21

Causality in Epidemiology

951
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...
951
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.0K
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,...
1.0K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

569
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:
569

You might also read

Related Articles

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

Sort by
Same author

Interpreting Breakthrough Infections Given Assortative Mixing of Partially Vaccinated Populations.

medRxiv : the preprint server for health sciences·2026
Same author

A century of weekly notifiable disease incidence data by province in Canada.

PLOS global public health·2025
Same author

Global stability of epidemic models with uniform susceptibility.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Investigating statistical power of differential abundance studies.

PloS one·2025
Same author

Using artificial intelligence tools to automate data extraction for living evidence syntheses.

PloS one·2025
Same author

A generalised catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2.

PloS one·2025

Related Experiment Video

Updated: Sep 23, 2025

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.7K

Testing and Isolation Efficacy: Insights from a Simple Epidemic Model.

Ali Gharouni1,2, Fred M Abdelmalek3, David J D Earn3,4

  • 1Department of Mathematics and Statistics, McMaster University, Hamilton, Canada. agharoun@uottawa.ca.

Bulletin of Mathematical Biology
|May 13, 2022
PubMed
Summary
This summary is machine-generated.

Epidemic modeling reveals that while testing intensity and speed can aid disease control, they may also reduce effectiveness. Focusing tests on infected individuals consistently improves control outcomes.

Keywords:
COVID-19EpidemiologyInfectious diseaseReproduction numberSARS-CoV-2Testing and isolation

More Related Videos

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.4K
A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii
09:17

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

Published on: January 2, 2017

14.8K

Related Experiment Videos

Last Updated: Sep 23, 2025

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
12:21

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

2.7K
Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.4K
A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii
09:17

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

Published on: January 2, 2017

14.8K

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Pathogen testing is crucial for managing epidemic spread.
  • Understanding individual testing processes' impact on population dynamics is key.
  • COVID-19 highlighted the need to analyze testing strategies.

Purpose of the Study:

  • To model how individual testing affects population-level epidemic spread.
  • To analyze the influence of testing intensity and focus on disease dynamics.
  • To derive an analytic expression for testing's effect on the reproductive number.

Main Methods:

  • Utilized a modified SIR (Susceptible-Infectious-Recovered) epidemiological model.
  • Incorporated per capita testing intensity and compartment-specific testing weights.
  • Derived an analytic expression for the basic reproductive number reduction due to testing.

Main Results:

  • Increased testing intensity and faster reporting can, under certain conditions, decrease control effectiveness.
  • Focusing testing on infected individuals consistently enhances control effectiveness.
  • Individual behavioral responses to testing information can influence outcomes.

Conclusions:

  • Testing strategies significantly impact epidemic control, with nuances in intensity and focus.
  • Targeted testing of infected individuals is a robust strategy for enhancing control.
  • Further research is needed to understand individual behavioral impacts on testing effectiveness.