Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

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:

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.However, realistic environmental conditions limit the number of...

Statistical Methods for Analyzing Epidemiological Data

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:

Viral Recombination

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.

Causality in Epidemiology

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...

Infectious Diseases and Their Occurrence

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...

You might also read

Related Articles

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

Sort by

Same author

Modelling the spread of two successive SIR epidemics on a configuration model network.

Journal of mathematical biology·2025

Same author

Possible counter-intuitive impact of local vaccine mandates for vaccine-preventable infectious diseases.

Mathematical biosciences and engineering : MBE·2024

Same author

Modelling: Understanding pandemics and how to control them.

Epidemics·2022

Same author

Commentary on the use of the reproduction number <i>R</i> during the COVID-19 pandemic.

Statistical methods in medical research·2021

Same author

The risk for a new COVID-19 wave and how it depends on <i>R</i> <sub>0</sub>, the current immunity level and current restrictions.

Royal Society open science·2021

Same author

Key questions for modelling COVID-19 exit strategies.

Proceedings. Biological sciences·2020

Search research articles

Related Experiment Video

Updated: Jul 13, 2026

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Published on: May 4, 2015

Reproduction numbers for epidemics on networks using pair approximation.

Pieter Trapman¹

¹Faculty of Veterinary Medicine, Utrecht University, The Netherlands. trapman@math.uu.nl

Mathematical Biosciences

|August 8, 2007

Summary

This study clarifies the pair approximation method for modeling epidemic spread on networks. New definitions for the reproduction number and growth rate parameter are introduced, improving accuracy for infection dynamics.

More Related Videos

Estimating Virus Production Rates in Aquatic Systems

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

Related Experiment Videos

Last Updated: Jul 13, 2026

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Published on: May 4, 2015

Estimating Virus Production Rates in Aquatic Systems

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

Area of Science:

Epidemiology
Network Science
Mathematical Biology

Background:

Pair approximation is a deterministic method for modeling epidemic spread in populations.
Existing methods face challenges in explicitly defining assumptions and key epidemic parameters.
Difficulties exist in defining the basic reproduction number (R(0)) and real-time growth rate (r) within the pair approximation framework.

Purpose of the Study:

To explicitly formulate the pair approximation method and its underlying assumptions.
To address and resolve issues in defining fundamental epidemic parameters within this method.
To introduce novel definitions for a reproduction number and a real-time growth rate parameter.

Main Methods:

Formal mathematical formulation of pair approximation techniques.
Explicit statement and discussion of assumptions inherent to the method.
Development of new definitions for key epidemiological metrics.

Main Results:

The study provides a clear formulation of pair approximation and its assumptions.
New definitions for a reproduction number and a real-time growth rate parameter are established.
An illustrative example demonstrates the improved accuracy of the approximated reproduction number compared to exact results.

Conclusions:

The refined pair approximation method offers a more rigorous approach to modeling infectious disease spread on networks.
The newly defined parameters enhance the quantitative analysis of epidemic dynamics.
This work provides a valuable tool for understanding and predicting infection transmission patterns.