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

Causality in Epidemiology01:21

Causality in Epidemiology

1.1K
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...
1.1K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

283
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:
283
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

282
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...
282
Immunological Memory01:23

Immunological Memory

13.4K
Immunological memory, a pivotal pillar of the adaptive immune system, is responsible for the body's ability to remember and respond more swiftly and effectively to previously encountered pathogens. This remarkable feature is what makes vaccines so effective in preventing diseases.
What is Immunological Memory?
Immunological memory is an integral function of the immune system that allows it to recognize and react more rapidly and effectively to pathogens previously encountered. This feature...
13.4K
Introduction to Epidemiology01:26

Introduction to Epidemiology

1.2K
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,...
1.2K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

663
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:
663

You might also read

Related Articles

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

Sort by
Same author

Time to collect informed consent to participate to randomized control trials in Intensive Care Unit patients: a single-center retrospective study over 7 years.

Minerva anestesiologica·2026
Same author

Exome sequencing directly implicates 68 genes in inflammatory bowel disease.

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

Modelling impacts of paediatric amoxicillin shortage management on pneumococcal resistance and invasive disease in Europe.

Nature communications·2026
Same author

Modelling the impact of ivermectin-based optimal strategies on malaria control: the role of formulation, coverage, and mosquitocidal efficacy timing.

EBioMedicine·2026
Same author

Age-dependent clinical and molecular rhinovirus epidemiology, 2018 to 2023.

The Journal of infectious diseases·2026
Same author

IDEA-FAST clinical study protocol: Identifying digital end-points of fatigue, sleep quality and daytime sleepiness in N = 2000.

Digital health·2026
Same journal

Spatio-temporal modeling of zoonotic cutaneous leishmaniasis (ZCL) in the Algerian steppe: Epidemiological insights and climatic associations.

Epidemics·2026
Same journal

Measuring the growth of infectious disease modelling publications and their impact on policymaking: A large language model-assisted bibliometric review.

Epidemics·2026
Same journal

Identifying memory mechanisms in Bayesian models of behavioural change during epidemics.

Epidemics·2026
Same journal

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 journal

A stochastic meta-population model of Ebola virus disease transmission for informing public health decisions.

Epidemics·2026
Same journal

Modelling serological cross-reactivity to disentangle the dynamics of West Nile and Usutu viruses in an emerging area.

Epidemics·2026
See all related articles

Related Experiment Video

Updated: Nov 5, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.1K

Memory is key in capturing COVID-19 epidemiological dynamics.

Mircea T Sofonea1, Bastien Reyné1, Baptiste Elie1

  • 1MIVEGEC, Univ. Montpellier, CNRS, IRD, France.

Epidemics
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

A new mathematical model accurately predicts COVID-19 spread by including infection age. Early lockdown in France significantly reduced the reproduction number, preventing thousands of hospital deaths.

Keywords:
Discrete-time modellingEpidemiosurveillanceMathematical epidemiologyNon-Markovian processesReproduction number

More Related Videos

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.5K
Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization
05:23

Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization

Published on: December 23, 2020

6.3K

Related Experiment Videos

Last Updated: Nov 5, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

3.1K
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.5K
Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization
05:23

Visualization of SARS-CoV-2 using Immuno RNA-Fluorescence In Situ Hybridization

Published on: December 23, 2020

6.3K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • The rapid global spread of SARS-CoV-2 (COVID-19) overwhelmed healthcare systems.
  • The lack of pre-existing immunity and presymptomatic transmission complicated control efforts.
  • Accurate mathematical models are crucial for informing effective public health policies during pandemics.

Purpose of the Study:

  • To develop a parsimonious discrete-time mathematical model for COVID-19.
  • To incorporate the effect of infection age on disease progression.
  • To analyze the COVID-19 epidemic in France and the impact of lockdown measures.

Main Methods:

  • Developed an original discrete-time mathematical model accounting for infection age.
  • Utilized nationwide time series data on hospital mortality and ICU activity in France.
  • Estimated key epidemiological parameters, including the reproduction number and lockdown impact.

Main Results:

  • The model incorporating "memory-effects" (age of infection) significantly improved fit to epidemiological data.
  • The French COVID-19 epidemic wave began around January 20th, with an initial reproduction number of approximately 2.99.
  • The national lockdown, initiated on March 17th, reduced the reproduction number by about 76%.

Conclusions:

  • Including infection age in COVID-19 models enhances predictive accuracy.
  • A one-week earlier lockdown implementation could have saved approximately 13,000 hospital deaths.
  • The developed model is valuable for real-time forecasting of community incidence and ICU demand.